TL;DR
This work proposes Disco4D, a self-disentangled pre-training to extract expressive 4D representations, to separate motion learning in the high-level latent space of the MAE bottleneck while performing geometric reconstruction in the low-level Euclidean space.
Point cloud upsampling is essential for 3D applications but remains challenging due to sparse, under-constrained observations. Multi-view images can resolve geometric ambiguity, but existing multi-modal methods do not fully exploit visual cues. We propose ControlFlow3D, a framework that distills knowledge from pretrain...
Self-supervised representation learning for 4D point cloud videos is challenging because annotations are costly and reconstruction-based pretraining can overemphasize low-level geometric details. We propose a JEPA-style framework that learns from unlabeled spatiotemporal point clouds through latent point-tube predictio...
ACE-Ego-Hand is introduced, an offline clip-level framework that extracts features via a Deterministic Clean-Latent Encoder and decodes them with a Bidirectional Spatiotemporal Decoder, offering a scalable path from everyday human video to robot manipulation data.
Recent advances in diffusion and flow models have substantially improved text-driven human motion generation. Yet most methods generate in low-dimensional, temporally downsampled latent spaces learned primarily for reconstruction, a bottleneck that can limit generation quality and preclude direct manipulation of indivi...
DSE-VTG is proposed, a \underline{D}ual-\underline{S}ide \underline{E}nhancement framework that addresses both temporal dynamics and task-specific training without any task-specific training, and achieves state-of-the-art performance among training-free methods.
TT-VidT leads Jester, Something-Something V2, ARID, and Diving48 fine-tuning simultaneously, improving over the strongest non-TT row by 54% ~ 121%, while using 48% fewer encoder FLOPs than DisMo and 55% fewer than VideoMAE or V-JEPA2.
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