1/23/2024 0 Comments Drag cars pic![]() The quickest of the heads-up Super classes(8.90 index), Super Comp is composed primarily of dragsters. Each class is assigned an index based on what a well-built car should run, and races are handicapped according to those indexes. Most cars are classified using a formula that divides total car weight by cubic inches. The engine combinations are just as diverse as the vehicles, from turbocharged 4- and 6-cylinder engines to Pro Stock-style V-8s and nitrous-oxide-equipped mountain motors. Dragsters, altereds, street roadsters, coupes, sedans, front-engine nostalgia dragsters, sport compact cars, and trucks race in 87 classes. No category in NHRA competition features more variety than Comp. A typical quarter-mile run is in the 5.2-second range at more than 270 mph. Like Top Fuelers, Top Alcohol Dragsters are restricted to a maximum wheelbase of 300 inches. Weights vary according to combination, but are generally between 1,975 and 2,125 pounds. The injected nitro cars do not use a transmission, and the supercharged cars have three forward speeds. Whereas Top Fuelers use supercharged, nitro-burning engines, Top Alcohol Dragsters may use a supercharged methanol-burning engine or an injected nitromethane combination. Top Alcohol Dragsters may look like Top Fuelers, but they have significant differences. Fuel injection is permitted, and spec gasoline is the only fuel allowed. To ensure a level playing field, the pushrod-equipped V-Twin engines are limited to 160 cubic inches, and the high-winding 4-cylinder engines cannot be larger than 101 cubic inches. The class features a wide variety of makes, models, and engines, including V-Twin entries from Harley-Davidson and Buell and inline 4-cylinder-equipped Suzuki and Kawasaki models. These highly modified vehicles, which can run under seven seconds at more than 195 mph on the quarter-mile, featuring a purpose-built tube chassis and a lightweight, aerodynamically enhanced replica of original bodywork. We also showcase the manipulation of real images through GAN inversion.Pro-stock-motorcycle PRO STOCK MOTORCYCLE Both qualitative and quantitative comparisons demonstrate the advantage of DragGAN over prior approaches in the tasks of image manipulation and point tracking. As these manipulations are performed on the learned generative image manifold of a GAN, they tend to produce realistic outputs even for challenging scenarios such as hallucinating occluded content and deforming shapes that consistently follow the object's rigidity. Through DragGAN, anyone can deform an image with precise control over where pixels go, thus manipulating the pose, shape, expression, and layout of diverse categories such as animals, cars, humans, landscapes, etc. To achieve this, we propose DragGAN, which consists of two main components including: 1) a feature-based motion supervision that drives the handle point to move towards the target position, and 2) a new point tracking approach that leverages the discriminative GAN features to keep localizing the position of the handle points. In this work, we study a powerful yet much less explored way of controlling GANs, that is, to "drag" any points of the image to precisely reach target points in a user-interactive manner, as shown in Fig.1. Existing approaches gain controllability of generative adversarial networks (GANs) via manually annotated training data or a prior 3D model, which often lack flexibility, precision, and generality. Synthesizing visual content that meets users' needs often requires flexible and precise controllability of the pose, shape, expression, and layout of the generated objects.
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