Reconstructing a physically stable human grasp from a single RGB image is challenging because physically modeling grasps is itself difficult, and the problem requires estimating not only a visually constrained hand pose but also a control target that stabilizes the grasp. Existing methods either model only visual hand...
Han Jiang, E. Vouga, Qi-Xing Huang et al.· 0 citations
Forecasting future 3D occupancy from surround-view cameras is essential for autonomous driving, yet existing approaches rely on dense voxel or bird's-eye-view representations whose cost grows rapidly with spatial resolution and prediction horizon. Because these representations do not explicitly maintain object identiti...
Hwanhee Jung, SeungHyeon Kim, Inkyu Koo et al.· 0 citations
Instruction-based video editing is commonly built on video-pretrained generative backbones: a video diffusion transformer is adapted, at considerable cost, to condition on a source video and an editing instruction. In this report we explore a different route and show that a strong instruction-based image editing model...
Yun-Peng Bai, Yossi Gandelsman, Michael Gharbi et al.· 3 citations
This paper investigates the connection between self-supervised spectral representation learning and diffusion generative models through a shared perspective on perturbation kernels and finds that the optimization of the spectral alignment objective is in an equivalent form of diffusion score distillation in the represe...
Yuehao Wang, Pei-Hao Wang, Hanwen Jiang et al.· 0 citations
This work revisits the role of positional encoding in video diffusion transformers and shows that it provides a useful spatial bias for geometry-aware control, and introduces a geometry-aware cross-attention mechanism that enables target video latent tokens to attend to structured context tokens derived from reference...
