GadNet: Garment Adaptive Network Optimization Dressing Human Animation

In virtual try-on, film, and gaming applications, the ability to depict realistic and convincing clothing behavior is crucial. Physics-based simulation demands high computational costs, while learning-based methods are limited to specific types of clothing. Although methods that directly reconstruct from images or monocular videos are more in line with consumer demands, existing approaches focus on capturing static or dynamic geometric features of clothing, neglecting the physical properties of clothing, resulting in unrealistic animations. Therefore, we propose GadNet. We separately reconstruct the body and clothing from the video, and then use our proposed motion sequence optimization module to learn plausible motion sequences, alleviating the frame jitter issue common in existing methods. Subsequently, we design a garment adaptive network. We employ a frequency-domain separation optimization method to process different frequency information of the clothing, thereby better subdividing the clothing mesh in high-frequency folding areas, enhancing the processing performance of clothing with different degrees of looseness. In summary, by incorporating physical constraints, we generate dynamic clothing models that both adhere to the animation effects of real video data and comply with physical constraints. Using video as input not only reduces costs but also better meets consumer demands. Additionally, it can be applied to body clothing animation in other datasets (e.g., CLOTH3D [1]). Our approach demonstrates reliable performance in unknown poses and clothing types, producing more realistic generated animations and satisfactory results, the advantages of GadNet are shown in Figure 1.