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Smart rice cookers, large-capacity commercial rice cookers, multifunctional electric cookers … In the online exhibition area of Canton Fair of Guangdong Huaqiang Electric Appliance Group Co., Ltd., a dazzling array of small household appliances attracted many buyers to stay in the exhibition area. "In the past few days, the company has reached cooperation with 12 customers, and the order amount is about 650,000 US dollars." Lu Runhua, general manager of the company, said with satisfaction.

From April 15th to 24th, China Import and Export Fair (Canton Fair) was held for the fourth time. The cumulative number of visitors in official website is 10.09 million, with 33.9 million visits; 536,000 overseas buyers from 228 countries and regions registered to watch the exhibition, which was 41.8% higher than the 130th session … The enthusiasm for participating in the exhibition was high and online transactions were busy. The "cloud" Canton Fair set up a unique economic and trade bridge for Chinese and foreign exhibitors and buyers.

Stable scale, more new products.

Canton Fair is regarded as a "barometer" and "weather vane" of foreign trade. The overall scale of the Canton Fair has remained stable, and the quality of exhibitors has steadily improved. According to Xu Bing, spokesman of Canton Fair, there are about 25,500 exhibitors at home and abroad, and 50 exhibition areas are set up according to 16 categories of goods. Domestic and foreign exhibitors uploaded a total of 3,052,100 exhibits, including 951,500 new products, all of which reached record highs.

Held continuously for more than 60 years, the Canton Fair has accumulated a large number of "fans". Although affected by the epidemic situation in COVID-19, exhibitors and buyers are enthusiastic and full of gains with the help of various new technologies and forms, such as pictures, texts, videos, 3D and VR.

"Six consecutive broadcasts a day!" Wang Dingying, deputy general manager of Ningbo Yongtaiping Import and Export Co., Ltd. said excitedly. The company’s products cover 14 exhibition areas of the Canton Fair, covering hardware, clothing, home accessories, shoes and bags, office appliances, consumer electronics, home appliances and many other categories. Wang Dingying introduced that the live broadcast of household appliances is the most popular, and new small household appliances such as coffee machines and wall-breaking machines were welcomed by buyers in India, the Philippines and other countries as soon as they appeared. "As long as customers need it, we are not afraid of being tired."

High-tech content and many new products are the highlights of this year’s Canton Fair.

On April 17th, at the launch event of new products in Canton Fair, a VR game device named "Lexiang Go-kart" attracted much attention. This product of Guangzhou Zhuo Yuan Virtual Reality Technology Co., Ltd. adopts the patent of crank technology, and brings people a realistic driving experience through the steering wheel, throttle, brake and blowing special effects. "With the guidance of’ fist products’, we have reached an intentional transaction amount of 360,000 US dollars at this Canton Fair." The person in charge of the company said.

Compared with previous years, more exhibitors launched their own research and development, independent brand exhibits, and made new achievements. Following the international fashion trend, the "high-value" aluminum cookware and the stainless steel cookware with thumb groove on bakelite handle … A large number of new products of Shanghai Longtou Import & Export Co., Ltd., a subsidiary of Dongfang International Group, have attracted many overseas buyers for their novel design, excellent materials and close market demand.

Practicing the concept of "creating green", green and low-carbon products have become a hot topic in this Canton Fair.

"Bamboo will not cause soil erosion when it is cut down, and its growth cycle is faster. It can replace trees as raw materials for production and play a role in protecting the environment and saving resources." Feng Limei, general manager of Foshan Meijia Green Environmental Protection Materials Co., Ltd. introduced that the company brought many new environmentally-friendly bamboo products at the Canton Fair, including degradable PLA cups and environmentally-friendly lunch boxes. "The epidemic has affected consumers’ living and eating habits, which has greatly increased the demand for disposable tableware, and our degradable tableware has gained a lot of negotiation intentions."

"Buy the world, sell the world" on the cloud.

"Cloud" exhibition, how to make the supply and mining better docking?

This year’s Canton Fair held 50 "Bridge of Trade" cloud promotion activities around the world, providing accurate docking services for multinational head enterprises and domestic key industrial clusters and brand enterprises, and enhancing the effectiveness of trade matching. The mascots of the Canton Fair "BEE" and "HONEY" became online recommenders, and eight trade promotion and matching activities of "Haobao and Haoni exploring and making friends" led millions of overseas fans to experience the Canton Fair in an immersive way. At the same time, the online platform was further optimized. For the first time, enterprise and product characteristic signs such as specialization, novelty, high-tech, green and low-carbon, and intelligent products were added, and the function of authorizing buyers’ information was added to make the platform more fluent and responsive.

In order to fit the characteristics of "cloud", exhibitors have adjusted their strategies to improve the "sense of presence" of product display through virtual reality and augmented reality technology. Ningbo Zhongji Huitong Group Co., Ltd. built a 3D online exhibition hall to provide buyers with a "360-degree no dead angle" exhibition experience. Ying Xiuzhen, vice president of the company, said: "Our exhibition hall has realized the dream linkage between virtual and reality. After wearing VR equipment, foreign businessmen can experience products more intuitively."

The Canton Fair moved to the "cloud", and exhibitors overcame the obstacles of time difference and distance to attract customers and open up markets.

Guangzhou Gao En Machinery Technology Co., Ltd. adjusts the online activity arrangement according to the time difference of customers, and informs new and old customers in advance. "The first online display after the opening ceremony lasted only 1 hour and 44 minutes, and the total number of likes reached 420,000. In the afternoon, the company arranged two online exhibitions, and soon received purchasing intentions from overseas buyers such as Mexico, Chile, India, Russia, Colombia, Spain and Germany. " Huo Liyi, general manager of the company, said.

According to the time difference of different customer groups, Haida Gate Control Co., Ltd. has set up a six-time online display. "In recent days, we have received dozens of customer inquiries, and the total amount of intentional orders has reached 148,000 US dollars." Liu Chunlin, the company’s foreign trade manager, said.

After watching the online exhibition of Oulusha Co., Ltd., Bangladeshi buyer Idis became interested in the bathroom cabinets and smart toilets produced by Oulusha, and finally reached the first intentional order of 300,000 US dollars. "Oulusha’s products are novel and full of design sense. We have learned about the whole company in detail in the online display, and we are also looking forward to visiting the factory in China after the epidemic." Idis said.

Overseas exhibitors are also full of gains. Spanish soap, Chilean red wine, Turkish coffee pot, Korean mask, German bearing … In the online import exhibition area of Canton Fair, 402 overseas enterprises from 32 countries and regions around the world exhibited many delicious, fun, good-looking and easy-to-use goods. Afghan exhibitor Ali brought a variety of high-quality products such as handmade blankets and pine nuts. He lamented: "China consumers have strong demand and purchasing power for high-quality specialty products."

According to statistics, during the current Canton Fair, the total number of visitors to the cloud exhibition hall of exhibitors was 6,223,100, and exhibitors held a total of 85,300 online exhibitions.

Unicom’s domestic and international double circulation

Recently, domestic epidemics have occurred frequently, and the production and business activities of some foreign trade enterprises have been limited, and logistics and transportation have been blocked. In this context, the significance of "Unicom’s domestic and international double cycle" in this Canton Fair has become increasingly prominent.

Expand the "circle of friends" to build a high-quality platform for foreign trade enterprises and create more business opportunities.

There are 170 global partners in this Canton Fair, including 50 partners from countries along the Belt and Road and more than 40 partners from RCEP member countries, achieving full coverage of RCEP member countries. "The Canton Fair is the golden signboard of China’s foreign trade development, and it has accumulated a huge credit advantage for more than 60 years. With the platform of the Canton Fair, we have established long-term cooperative relations with many domestic and foreign customers. " Lin Xiaofeng, vice president of overseas marketing of Shenzhen Tuori New Energy Technology Co., Ltd. said.

Improve the support ability and facilitate enterprises to "go out to sea".

In order to alleviate the difficulties of foreign trade enterprises in "finding ships" and "finding cabinets", this year’s Canton Fair has set up a trade service section, which is divided into four categories: testing and certification, cross-border e-commerce, logistics and transportation, and comprehensive services, covering 26 logistics and transportation enterprises, 7 cross-border e-commerce enterprises and 4 comprehensive service enterprises, and set up a cross-border e-commerce area to escort the order fulfillment of participating foreign trade enterprises.

Promote the integration of domestic and foreign trade, and help foreign trade enterprises to "walk on two legs" in export and domestic sales.

Shanghai Guanhua Stainless Steel Products Co., Ltd., a veteran foreign trade enterprise, was hit by the epidemic, and some orders were cancelled or delayed, resulting in a backlog of inventory. Through the Canton Fair, enterprises can get to know more domestic buyers, build their own brands and explore the domestic market. At present, the company’s domestic products account for 20% of the total sales.

Behind the strong popularity of the Canton Fair is the China market with unlimited business opportunities and the resilient China economy.

For seven consecutive quarters, the year-on-year growth was positive. In the first quarter, the total import and export value of goods trade was 9.42 trillion yuan, up 10.7% year-on-year … The outstanding performance of China’s foreign trade has conveyed confidence and strength to Chinese and foreign exhibitors in this Canton Fair. Becca Meka Belize, president of the Georgian Chamber of Commerce and Industry in Guangdong Province, believes that the "cloud" Canton Fair has provided solid support for ensuring the stability of the global industrial chain supply chain and injected strong impetus into the global economic recovery. "The creative Cloud Canton Fair fully embodies the wisdom of China, which has opened up new trade channels for me. The market in China is very broad, and I have sufficient confidence to dig deep in this layout. " Becca said.

In the long run, how to build a "Digital Canton Fair" with high standards and high quality? Wang Shouwen, Vice Minister of Commerce, said that China will open wider to the outside world, and the Canton Fair will also play a more important role in this process. In the future, the online exhibition of the Canton Fair will innovate mechanisms, enrich formats and expand functions, and realize the integrated development of online and offline. "The Canton Fair will continue to improve the service level for exhibitors and strive to provide more display channels and service content; Continue to increase the invitation of professional buyers, and strive to achieve the increase of buyers’ participation enthusiasm, stable quantity, optimized structure and improved quality; Continue to create a safe, reliable and mutual trust exhibition environment, and strive to make the platform more stable, more functional and more effective. " Wang Shouwen said. (Reporter Wang Wenzheng)

Recently, Ashok Elluswamy, director of Tesla autopilot software, gave a speech at CVPR 2022 conference, introducing many achievements made by Tesla autopilot team in the past year, especially the neural network model named Occupancy Networks (hereinafter referred to as occupation network).

He said that there are many problems in the traditional methods of semantic segmentation and depth information used in autonomous driving systems, such as the difficulty of converting 2D to 3D and the inaccurate estimation of depth information.

After using the occupancy network, the model can predict the space occupied by objects around the vehicle (including the space generated by the next action of dynamic objects).

Based on this, vehicles can make evasive actions without identifying the specific obstacles-Ashok Elluswamy even joked on Twitter that Tesla’s car can even avoid UFOs!

Based on this technology, vehicles can also see whether there are obstacles in the surrounding corners, so as to realize unprotected steering like human drivers!

In short, the occupation of the network has significantly enhanced Tesla’s autonomous driving ability (L2).

It is said that Tesla’s autopilot system can prevent 40 car accidents caused by driver errors every day!

In addition, Ashok Elluswamy also highlighted the efforts of Tesla Autopilot system in preventing drivers from misoperation.

By sensing the external environment and the driver’s operating system, the vehicle can identify the driver’s misoperation, such as stepping on the accelerator pedal at the wrong time, the vehicle will stop accelerating and brake automatically!

▲ Tesla active braking

That is to say, some problems of "brake failure" caused by driver’s misoperation that have been frequently exposed in China will be technically restricted.

I have to say that Tesla is really good at promoting technological progress. The following is a video compilation of Ashok Elluswamy’s speech, with a slight deletion.

First, the pure vision algorithm is powerful, and the two-dimensional image is transformed into three-dimensional.

At the beginning of the speech, Ashok said that not everyone knows the specific functions of Tesla’s autopilot system, so he briefly introduced it.

▲Ashok

According to him, Tesla’s autopilot system can help vehicles achieve lane keeping, vehicle following, deceleration and cornering, etc. In addition to these, Tesla’s autopilot system is also equipped with standard safety functions, such as emergency braking and obstacle avoidance, which can avoid a variety of collisions.

In addition, since 2019, about 1 million Tesla vehicles can use more advanced navigation on the expressway, check the information of adjacent lanes to perform lane changes and identify the entrances and exits of the expressway.

Moreover, the Tesla autopilot system can automatically park in the parking lot, identify traffic lights and road signs, and make a right turn to bypass obstacles such as cars. At present, these functions have been verified by hundreds of thousands of Tesla owners.

In the speech, Ashok also took out a video recorded by a user. The video shows that when the user is driving on a crowded road in San Francisco, the car screen shows the surrounding environment, such as road boundaries, lane lines, the position and speed of nearby vehicles.

▲ The system recognizes the surrounding environment.

On the one hand, these need the support of hardware such as Tesla car and camera, on the other hand, they also need the support of algorithms and neural networks built into Tesla autopilot system.

According to Ashok, Tesla is equipped with eight 1.2-megapixel cameras, which can capture 360-degree images of the surrounding environment and generate an average of 36 images per second. Then Tesla’s car will process this information, and it can perform 144 trillion operations per second (TeraOPs/s).

Moreover, these processes are based on pure vision algorithm, without using lidar and ultrasonic radar, and without high-definition map.

So how does Tesla’s autopilot system identify general obstacles?

Ashok said that when encountering general obstacles, the system will use the space division method. When using the space segmentation method, the system marks every pixel in the space as "drivable" and "non-drivable", and then automatically drives the chip to deal with the scene. However, there are some problems with this method.

▲ Marking of objects

First of all, the object pixels marked by the system are in two-dimensional space, and in order to navigate the car in three-dimensional space, the object pixels need to be converted into the corresponding predicted values in three-dimensional space, so that Tesla’s system can establish an interactive physical model and handle the navigation task smoothly.

▲ Marking of objects

However, when the system converts the pixels of an object from a two-dimensional image to a three-dimensional image, it needs to carry out image semantic segmentation (that is, identify the image at the pixel level, that is, label the object category to which each pixel in the image belongs).

This process will produce unnecessary images or unnecessary pixels in the system, and a few pixels on the ground plane of the image can have a great influence, which directly determines how to convert this two-dimensional image into a three-dimensional image. Therefore, Tesla does not want to produce such influential pixels during planning.

In addition, different obstacles need to be judged by different methods.

Generally speaking, the depth value of the object (the distance from the observer’s point of view to the object, which is finally obtained after projection transformation, standardization of equipment coordinates and scaling translation) is commonly used.

In some scenarios, the system can predict obstacles first. In another scene, the system can also detect the depth of the pixels of the image, so each pixel will produce some depth values.

▲ Depth map (right)

However, although the depth map finally generated is very beautiful, only three points are needed when the depth map is used for prediction.

Moreover, when visualizing these three points, although it is ok to look at them at close range, they will also be deformed with the increase of distance, and it is difficult to continue to use these images in the next stage.

For example, walls may be deformed and bent. The objects near the ground level are also determined by fewer points, which makes the system unable to correctly judge obstacles in planning.

Because these depth maps are converted from plane images collected by multiple cameras, it is difficult to produce the same obstacle in the end, and it is also difficult for the system to predict the boundary of the obstacle.

Therefore, Tesla proposed an occupation network scheme to solve this problem.

Second, calculate the space occupancy rate and code the object.

During the speech, Ashok also showed this network occupation scheme by video. He said that as can be seen from the video, in this scheme, the system processes the images captured by eight cameras, then calculates the space occupancy rate of the object, and finally generates a schematic diagram.

▲ The generated simulated image

Moreover, every time the Tesla car moves, the system network will recalculate the space occupancy rate of the surrounding objects. In addition, the system network will not only calculate the space occupancy of some static objects, such as trees and walls, but also calculate the space occupancy of dynamic objects, including driving cars.

After that, the network outputs the image into a three-dimensional image, and can also predict the occluded object, so even if the car only uploads part of the outline of the object, the user can distinguish the object clearly.

In addition, although the resolution of the images captured by the system is different because of the distance, based on the above scheme, the resolution of the simulated 3D images finally presented is the same.

▲ The generated images have the same resolution.

This means that the whole scheme runs very efficiently. Ashok said that the computing platform runs for 10 milliseconds, and the system network can run at the rate of 100 Hz, which is even faster than the speed of many cameras recording images.

So, how is this process completed? This requires an understanding of the architecture of the occupation network scheme.

When explaining the architecture of the occupation network scheme, Ashok takes the images taken by Tesla fisheye camera and the left camera as an example, and compares the image correction process between them.

First, the system will stretch the image, then extract the image features, find out whether the points related to the 3D image are occupied, then use the 3D position coding, and then map it to a fixed position, after which the information will be collected in the subsequent calculation.

▲ Conduct preliminary processing on the image.

After that, the system will embed the position of the image space, continue to process the image stream through 3D query, and finally generate 3D occupation features. Because high-dimensional occupation features are generated, it is difficult to perform this operation at every point in the space. Therefore, the system will generate these high-dimensional features in lower dimensions, such as using typical up-sampling technology to generate high-dimensional space occupancy.

▲ Calculate the space occupancy of objects.

Interestingly, Ashok revealed in his speech that this occupation network scheme was only used to deal with static objects, but in the end it was found that it was difficult to deal with only static trees, and the system also encountered many difficulties when it first began to distinguish between "true and false pedestrians".

But the team finally found that no matter whether these obstacles are moving or static, the system only needs to be able to avoid them in the end.

▲ True and false pedestrians

Therefore, the occupation network scheme no longer distinguishes between dynamic obstacles and static obstacles, but uses other classifications to deal with them and calculate the instantaneous space occupancy rate of objects, but this is not enough to ensure that Tesla cars can drive safely.

Because if only the instantaneous space occupancy rate is calculated, it is not reasonable for Tesla to meet a car while driving at high speed and then start to slow down. The system wants to know more about the space occupancy rate of this car at different times and the changes.

In this way, the system can predict when the car will leave. Therefore, the scheme also involves predicting the occupied flow.

▲ Calculation process of occupied flow

The data of occupancy flow can be the first and higher derivative of space occupancy or time, and it can also provide more accurate control and unify them in the same coordinate system. The system will use the same method to generate space occupancy and occupancy flow, which will also provide strong protection against various obstacles.

Third, the type of obstacle is not important. The system can avoid collision.

Ashok also said that conventional motion or mobile networks can’t judge the type of object, such as whether it is a static object or a moving vehicle.

However, from the control level, the type of objects is actually not important, and the network occupation scheme provides good protection to prevent the network from classification difficulties.

Because no matter what the obstacle is, the system will think that this part of the space is occupied and move at a certain speed. Some special types of vehicles may have strange protrusions, which are difficult to model with traditional technology. The system will use cubes or other polygons to represent moving objects.

In this way, the object can be squeezed at will, and this occupying method does not need complex grid topology modeling.

When the vehicle is turning unprotected or protected, geometric information can be used to infer the occlusion situation. Geometric information should not only infer the information recognized by the vehicle camera, but also infer the unrecognized information.

For example, when a car is making an unprotected turn, there is a fork in the road ahead, and there may be potential vehicles blocked by trees and road signs, so the car "knows" that it can’t see the vehicles from these obstructions. Based on different control strategies, cars can ask questions and eliminate this occlusion.

Therefore, for a stationary object, the car can recognize when it becomes visible while driving. Because there is a complete three-dimensional obstacle, the car can also predict what distance it will hit this object, and then the system will identify and pass this blocked object through smooth control.

Therefore, the occupy network scheme helps to improve the control stack in many different ways. This scheme is an extension of the nerve radiation field, and the nerve radiation field (NeRf) has largely taken over the research of computer vision in the past few years.

▲ Schematic diagram of the association between Nerf and occupied network

NeRf is an image reconstruction scene in a single scene or a single location, which is reconstructed from a point in a single location.

Ashok said that when Tesla’s vehicles are driving, the images received in the background processing are more accurate, so it is possible to generate cross-time and accurate image routes, and generate more accurate 3D reconstruction through the NeRf model and 3D state differential rendering images.

There is a problem with the images in the real world-we will see a lot of unreal or different scenes in the real world.

For example, the glare of the sun or the dirt or dust on the windshield will change due to the diffraction of light, or raindrops will further distort the propagation of light and eventually produce artifacts.

The way to improve the robustness is to use higher-level descriptors, but these descriptors will not change the local lighting artifacts (such as glare) to a certain extent.

Because RGB (color system) images may be very noisy, adding descriptors on RGB can provide a layer of semantic protection to prevent the change of RGB values. Therefore, Tesla’s goal is to use this method to occupy the network solution.

▲ Descriptors are more robust than RGB.

Because the network occupation scheme needs to generate space occupancy in several shots, it cannot run a complete neural optimization in the car, but the neural optimization can be reduced to run in the background, ensuring that the space occupancy generated by it can explain the images of all sensors received by the car when it is running.

In addition, descriptors can also be superimposed during training to produce good supervision for these networks; At the same time, different sensor data can be differentiated to monitor the images held.

At present, Tesla has a network to reduce obstacles. The next step is to avoid any collision. Autopilot already has many safety functions.

Then, Ashok showed three videos of Autopilot starting to avoid collision.

The collision accident here refers to the collision accident caused by the driver accidentally stepping on the accelerator pedal as a brake pedal.

Ashok means that when the driver accidentally uses the accelerator as a brake, the car will accelerate and cause a collision, but the car will recognize and automatically stop the acceleration and automatically brake to prevent the collision.

In the first video, Ashock said that if Autopilot didn’t start and stopped the car from accelerating, the driver in the video would probably fall into the river.

▲ Tesla AP starts to avoid the car falling into the river.

Similarly, the second video shows that a Tesla driver mistakenly stepped on the accelerator while parking, but Autopilot quickly started and prevented the car from hitting shops and pedestrians.

▲ Tesla AP starts to avoid the car hitting the store.

Fourth, automatically plan the path through the occupied vehicle.

However, it may take several seconds or even minutes for the car to brake and stop smoothly, and there may not be enough time to identify obstacles and calculate when the car is driving.

So we should use neural network to achieve this goal; Especially recently, there have been more complicated hidden scenes. What Tesla’s autonomous driving team has to do is to get the space occupancy rate from the previous network.

First, the space occupancy rate should be coded into a super-compressed MLP. In essence, this MLP is an implicit representation of whether collision can be avoided in any specific query state, and this collision avoidance method provides some guarantees in a certain time range. For example, collisions can be avoided in 2 seconds or 4 seconds or within a certain time range.

Ashok gives another example here. He gives a top-down road. Black pixels are obstacles, gray pixels are roads and white pixels are road lanes. In the top view of this three-dimensional space, the car can be placed at any pixel position to simulate whether the collision can be avoided.

▲ Schematic diagram of vehicle driving situation

He said: "If you think of the car as a single point and the period of avoiding collision is set to an instant, then whether there will be a collision at the current time depends only on the position of the obstacle; But the problem is that the car is not a point, it has a rectangular shape and can also turn. "

Therefore, only when the shape is convolved with the obstacle can we immediately know whether the car is in a collision state.

As the car turns (or rotates out of control), the collision field will change. Green means that the car is in a safe position without collision, and red means collision, so when the car rotates, there are more collision positions; But when the car position is aligned, the green position expands, which means that the car will not collide.

On the whole, Ashok shows how to use multiple camera videos and products to generate dense space occupation rate and occupation flow. Through space occupation rate, an effective collision avoidance field can be generated through neural network, that is, vehicles can "see" through the camera and, according to experience, pass through the road with appropriate speed and direction.

▲ Implicit neural network for collision avoidance.

Ashok also shared an experiment in a simulated environment. The driver stepped on the accelerator to accelerate and there was no steering behavior. The car monitored that there would be a collision and planned a path to make the car pass safely.

Ashok said at the end of his speech that if they can successfully implement all the above technologies, they can produce a car that will never crash.

Obviously, this work has not been completed. In his last PPT, Ashok actively invited engineers to join Tesla and build a car that will never crash!

▲ Ashok Elluswamy welcomes more talents to join Tesla.

Conclusion: Tesla continues to explore autonomous driving.

Since Tesla’s self-driving technology with fire, a large number of followers have emerged on the self-driving track. But it must be said that Tesla is still at the forefront of the industry and constantly explores new methods of autonomous driving.

The person in charge of the Tesla Autopilot project brought new technical interpretations, and also showed us the highlights of Tesla’s future autonomous driving technology in advance to some extent. With Tesla’s spirit of continuous exploration, its autonomous driving will continue to lead the entire automobile market.

　 　 Spring Festival holiday has come, are you still at home? A trip without a visit? (because of poverty)

　 　 During the 7-day long vacation, you must have arranged a near or far travel plan. Whether it’s the romance between two people or the excitement between relatives and friends, I believe everyone hopes to leave the most beautiful moment in the journey with a camera.

　 　 A capital BUT!

　 　Unfortunately, although the scenery is good, there are always pig teammates in the same industry, and the photos taken make people stunned, depressed, sad and angry … …

You actually look like this.

But in their lens, you become like this … …

　 　 Nowadays, sighing at the sky doesn’t help. How can we improve their photography level? Xiao Dianjun tells you a good way — — Forward this content to him/her, and make sure to get good photography skills quickly after reading it, and take beautiful photos like movie pictures on the trip ~

　 　 Our slogan is: Watch movies and celebrate the Year of Bigger!

I am not single dog, I want to be wonderful alone.

　 　 Taking advantage of this holiday, traveling with my other half is the happiest thing, but we have all had the painful experience of "my boyfriend took me like this". No matter how to pose in front of the camera, the final photo is always unsatisfactory.

　 　 In movies, there are many lonely scenes depicting the protagonist alone, which have both temperament and connotation, and the pictures are not conventional. Although we don’t have the photographic skills of masters for decades, we may be able to succeed more than half by learning a little "fur" from these stills and grasping the pose of the characters and the composition of the pictures in the lens!

Reference film: Nie Yinniang the Assassin.

Applicable scene: Woods and reeds.

Tip: Stand sideways among the trees and look into the distance. The focus of the lens is on your body, using scattered branches to create a virtual and real effect.

Reference film: Mars Rescue

Applicable scenes: desert, square, open space

Skill: Try to choose an empty venue, sit down naturally on the slope, and look straight ahead or bow your head slightly. The figure is located in 1/5 of the picture.

Reference film: The Resurrection Man

Applicable scenes: outdoor and suburban areas

Skill: At sunset, look up at the sky 45 on your side, and make a bitter hatred like a small plum.

Reference film: mr. six.

Applicable scenes: outdoor, ice.

Important skills: Turn your back to the camera, and hang your hands naturally or put them in your pockets (you can also imitate the posture of mr. six code rack if you want to shoot a very man effect). Photographers need to squat (pa) and shoot from bottom to top.

The most romantic thing I can think of is to pat with you.

　 　 (single dog, please consciously ignore it) Both people’s time is sweet and happy. Whether they are lovestruck couples or model couples who have been married for many years, they can leave precious memories about each other through a small trip.

　 　 Even if there is no third party on the trip, you can make good use of the timing shooting function of tripod and camera. As long as you master the skills, you can shoot a couple’s blockbuster anywhere.

Reference film: "Searching for Dragons"

Applicable scenes: grassland, convertible.

Tip: After putting the camera in place, neither of them looks directly at the camera and looks far away. Boys can also look up at the sky with their heads in their hands like Hu Bayi, and make a "zhuang" and sprinkle "X".

Reference film: Brooklyn

Applicable scenario: anywhere

Skill: Also use far and near stations to focus on the people in front (or behind) to create the effect of reality.

Reference film: "Mistakes in the Stars"

Applicable scenes: lawn, snow

Skill: I believe every girl has fantasized about this classic picture, but it is not difficult to realize it — — Set up a solid tripod, put the camera down and set the shooting time! But remember to check the picture in advance to succeed once. (It will be better if we can find a third person to stand on a high place and shoot down.)

Reference film: My Girlhood

Applicable scene: selfie

Skill: Of course, girls should be beautiful. Just make a face and let your boyfriend do it!

Reference film: The Theory of Everything.

Applicable scene: a relatively empty field in the suburbs

Tip: This movie is simply a textbook for shooting couples’ photos. Set up a tripod, set up the continuous shooting function, look at each other’s eyes, and slowly turn around ~ turn ~ get up ~

Leave the happiest smile of parents and elders.

　 　 Of course, the Chinese New Year is a time to reunite with family members. When you get together with brothers and sisters, parents and relatives you haven’t seen for a long time, it is natural to take a group photo as a souvenir, and then organize a group outing to experience the warmth of family ties.

　 　 The family is sitting in front of the camera, and it is really boring to shout "eggplant" in a serious way. Xiao Dianjun only teaches you three tricks, so that you and your family can leave the most unique, fun and warm image memories.

Reference film: "Beautiful Day"

Applicable scene: outdoors, it is better when the weather is sunny.

Tip: Choose a sunny day, and the family will get together and pose in different poses. The camera can be placed directly on the ground and the lens will shoot upwards.

Reference film: Return to 20 years old

Applicable scene: indoors

Tip: Prepare a set of clothes similar to grandpa’s/grandma’s, pose the same as them, even if it is just a pose facing the camera, it can leave the warmest image.

Reference film: "The Bailey Family"

Applicable scene: any outdoor scenic spot

Skill: There is no special skill, just a little closer and a little closer. It is the best moment for a family to hug together.

Friends get together, no madness, no youth.

　 　 Spring Festival holiday can get rid of the complicated daily work, and it is also the best time to get together with friends. Friends of 3579 go hand in hand, and taking some exciting photos will make the atmosphere more warm. Maybe after a few years, I will dig out this photo and recall the madness when I was young.

　 　 There are even more plots describing friendship in movies. Their stories must be more dramatic than our daily life, and they are also quite literary and dramatic under the camera record, which may add some fun.

Reference film: Wild Horse

Applicable scenes: crowded cafes, sofas and cars.

Tip: If literary girls want to take a group of cool party photos, they can try to squeeze 4 or 5 people into a narrow space, which can be a two-person sofa in a coffee shop or a back seat in a car. They may snuggle up and look at each other, which may have a different effect.

Reference film: diary of the sea street

Applicable scene: seaside and suburban evening.

Skill: Feel the reunion with friends, laugh happily, and smile from the heart will become the most beautiful freeze-frame picture.

Reference film: My Girlhood

Applicable scenario: any open field

Tip: Sound waves ~ too strong ~ will be hit to the ground if you don’t shake them! Play a high song and swing with the music! Set the continuous shooting function and the camera will automatically record these happy moments.

Yamagata poster

Japanese actress Riko Narumi, actor Itsuki Sawamura and Naoto Takenaka attended the Paris Fashion Exhibition held in Paris on 25th. On the same day, Riko Narumi, who was only 16 years old, showed off a modeling step in order to promote the new film, and won the applause of the audience.

As the "Paris Fashion Exhibition" is the forerunner of the new fashion trend in the world, it attracted nearly 300 media from all over the world for interviews that day. Naoto Takenaka directed and acted his new film Yamagata to promote it. On the same day, Naoto Takenaka, Itsuki Sawamura and Riko Narumi, starring in the film, dressed in costumes, appeared on the catwalk. The reason why three people are invited to the fashion show is that all the clothes in the upcoming film Yamagata are sponsored by the world-famous clothing brand "MASATOMO".

Riko Narumi

At the scene, I saw Riko Narumi, the heroine who plays a high school student in the film, dressed in a costume, Itsuki Sawamura, who plays a samurai, dressed in armor, and Naoto Takenaka, the director, appeared in a leopard suit. The three of them walked like cats, which undoubtedly became the focus of the flash that day. Although all three people showed their self-confidence when they walked on the runway for the first time in subsequent interviews, many celebrities in the fashion industry still gave warm applause.

In addition, in the interview, Riko Narumi expressed his nervousness when he first appeared on the runway, and hoped that the French audience would like the movie Yamagata by showing their costumes on the runway.

It is reported that the horror and comedy film Yamagata is expected to be released in Japan this summer.

Next page: More wonderful pictures.

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