Registering clothes from 4D scans with vertex-accurate correspondence is challenging, yet important for dynamic appearance modeling and physics parameter estimation from real-world data. However, previous methods either rely on texture information, which is not always reliable, or achieve only coarse-level alignment. In this work, we present a novel approach to enabling accurate surface registration of texture-less clothes with large deformation. Our key idea is to effectively leverage a shape prior learned from pre-captured clothing using diffusion models. We also propose a multi-stage guidance scheme based on learned functional maps, which stabilizes registration for large-scale deformation even when they vary significantly from training data. Using high-fidelity real captured clothes, our experiments show that the proposed approach based on diffusion models generalizes better than surface registration with VAE or PCA-based priors, outperforming both optimization-based and learning-based non-rigid registration methods for both interpolation and extrapolation tests.

Despite recent progress in developing animatable full-body avatars, realistic modeling of clothing - one of the core aspects of human self-expression - remains an open challenge. State-of-the-art physical simulation methods can generate realistically behaving clothing geometry at interactive rates. Modeling photorealistic appearance, however, usually requires physically-based rendering which is too expensive for interactive applications. On the other hand, data-driven deep appearance models are capable of efficiently producing realistic appearance, but struggle at synthesizing geometry of highly dynamic clothing and handling challenging body-clothing configurations. To this end, we introduce pose-driven avatars with explicit modeling of clothing that exhibit both photorealistic appearance learned from real-world data and realistic clothing dynamics. The key idea is to introduce a neural clothing appearance model that operates on top of explicit geometry: at training time we use high-fidelity tracking, whereas at animation time we rely on physically simulated geometry. Our core contribution is a physically-inspired appearance network, capable of generating photorealistic appearance with view-dependent and dynamic shadowing effects even for unseen body-clothing configurations. We conduct a thorough evaluation of our model and demonstrate diverse animation results on several subjects and different types of clothing. Unlike previous work on photorealistic full-body avatars, our approach can produce much richer dynamics and more realistic deformations even for many examples of loose clothing. We also demonstrate that our formulation naturally allows clothing to be used with avatars of different people while staying fully animatable, thus enabling, for the first time, photorealistic avatars with novel clothing.

This paper studies the problem of inverse cloth simulation—to estimate shape and time-varying poses of the underlying body that generates physically plausible cloth motion, which matches to the point cloud measurements on the clothed humans. A key innovation is to represent the dynamics of the cloth geometry using a dynamical system that is controlled by the body states (shape and pose). This allows us to express the cloth motion as a resultant of external (skin friction and gravity) and internal (elasticity) forces. Inspired by the theory of optimal control, we optimize the body states such that the simulated cloth motion is matched to the point cloud measurements, and the analytic gradient of the simulator is back-propagated to update the body states. We propose a cloth relaxation scheme to initialize the cloth state, which ensures the physical validity. Our method produces physically plausible and temporally smooth cloth and body movements that are faithful to the measurements, and shows superior performance compared to the existing methods. As a byproduct, the stress and strain that are applied to the body and clothes can be recovered.

Video style transfer aims to synthesize a stylized video that preserves the content of a given video and is rendered in the style of a reference image.A key issue in video style transfer is how to balance video content preservation and reference style rendering, in order to avoid over-stylization with serious video content loss or under-stylization with unrecognized reference style. In this demonstration, we illustrate a novel video style transfer application, named HeterStyle, which can stylize different regions in the video with adaptive intensities.The core algorithm of HeterStyle application is our proposed heterogeneous video style transfer method, which minimizes a heterogeneous style transfer loss function considering content, style and temporal consistency in a Convolutional Neural Networks based optimization framework.With the HeterStyle application, a user can easily generate the stylized videos with good video content preservation and reference style rendering.

Salient object detection aims to detect the attractive objects on images and videos. In this paper, we propose a novel salient object detection method for videos based on cross-frame cellular automata. Given a video, we first represent the video frames with super-pixels, and construct a saliency propagation network among super-pixels within a frame and between adjacent frames based on their appearance similarities and temporal coherency. Second, we initialize the saliency map of each frame with the fusion of two saliency maps generated by appearance and motion features independently. Finally, we utilize cellular automata updating to propagate saliency among super-pixels iteratively and generate the coherent saliency maps with complete objects. The experimental results show that our method outperforms the state-of-the-art methods on different types of videos.

As a bridge to connect vision and language, visual relations between objects in the form of relation triplet $łangle subject,predicate,object\rangle$, such as "person-touch-dog'' and "cat-above-sofa'', provide a more comprehensive visual content understanding beyond objects. In this paper, we propose a novel vision task named Video Visual Relation Detection (VidVRD) to perform visual relation detection in videos instead of still images (ImgVRD). As compared to still images, videos provide a more natural set of features for detecting visual relations, such as the dynamic relations like "A-follow-B'' and "A-towards-B'', and temporally changing relations like "A-chase-B'' followed by "A-hold-B''. However, VidVRD is technically more challenging than ImgVRD due to the difficulties in accurate object tracking and diverse relation appearances in video domain. To this end, we propose a VidVRD method, which consists of object tracklet proposal, short-term relation prediction and greedy relational association. Moreover, we contribute the first dataset for VidVRD evaluation, which contains 1,000 videos with manually labeled visual relations, to validate our proposed method. On this dataset, our method achieves the best performance in comparison with the state-of-the-art baselines.