Monocular Video-to-4D generation faces the fundamental challenge of inferring plausible 3D geometry and motion from limited single-view inputs. We present MotionStruct4D, a novel approach that discovers and exploits the underlying motion structure of 3D Gaussian Splatting for high-quality video-to-4D generation. Our key insight is that real-world motion can be effectively decomposed into coarse rigid transformations that capture principal movements, complemented by detailed non-rigid deformations that account for fine-grained details. MotionStruct4D introduces: (1) a self-supervised motion structure discovery module that identifies quasi-rigid parts by preserving spatiotemporal relationships without explicit 3D supervision, and (2) a weighted dense-to-sparse optimization architecture that transitions from dense per-Gaussian deformation to sparse control points, effectively integrating rigid and non-rigid motion components through adaptive weighted fusion. This design addresses the parametric imbalance between rigid and non-rigid motions and effectively models 3D movements across different kinematic patterns. To evaluate our approach on challenging scenarios, we curate a comprehensive benchmark dataset featuring substantial object displacement and diverse articulated motion patterns. Experimental results demonstrate MotionStruct4D’s superior performance in motion fidelity and novel viewpoint synthesis quality, while also providing interpretable motion structure decomposition that reveals meaningful quasi-rigid part segmentation.
Jia-Xing Zhong, Kai Lu, Jiao-Jiao Ye et al.· International Journal of Com...· 0 citations
Vision-language-action (VLA) models provide a scalable path toward generalist robotic manipulation by integrating visual perception, language understanding, and continuous action control. However, we reveal a critical limitation of VLA architectures: the action expert has limited access to the 3D geometric and 2D semantic information available in VLM features. This accessibility gap weakens perceptual grounding and limits performance on fine-grained robotic manipulation. To address this issue, we propose V-Link, which explicitly recovers visual representations during the vision-language (VL) to action (A) feature transfer. Specifically, V-Link learns complementary Spatial and Semantic Query representations within the VLM and injects them into Action DiT through asymmetric pathways. Semantic Queries complement the original VLM image tokens, whereas Spatial Queries provide dedicated geometric conditioning for spatially grounded action generation. Across LIBERO, LIBERO-Plus, and RoboTwin 2.0, our V-Link improves the average success rate over base model GR00T N1.6 by +1.9%, +31.2%, and +18.8%, respectively. On the AGIBOT A3 Ultra, V-Link further achieves gains of +20% and +24% on two real-world humanoid tasks.
Yehao Lu, Jia-Rui Yang, Yu-Ning Su et al.· 0 citations
Vision-language-action (VLA) models leverage pretrained vision-language representations for robot control, yet simply adding historical frames does not reliably capture recent physical change. This is especially problematic in multi-stage manipulation, where visually similar states may require different actions depending on prior execution. To address this challenge, we present TemporalFlow-VLA, which learns compact execution history through physically grounded temporal supervision. Using recorded robot states, robot geometry, and calibrated cameras, we construct robot-surface temporal flow as a training-only target and supervise two execution-aligned temporal queries that provide structured history to the action expert. The geometric supervision path is not evaluated at deployment. TemporalFlow-VLA achieves 97.63 +/- 0.26% average success on LIBERO, including 96.60 +/- 0.87% on LIBERO Long, and 85.5%/84.2% Clean/Randomized success across 12 RoboTwin tasks. It shows its clearest advantage over prior methods on longer-horizon, multi-stage manipulation. Controlled history interventions show that action prediction depends on both historical content and temporal order. With asynchronous feature caching, temporal conditioning maintains single-frame-level server-side sampling latency without additional historical-encoding overhead. Overall, TemporalFlow-VLA provides a compact, physically grounded interface for exploiting ordered execution history without explicit motion estimation or geometric processing at deployment.
Jiarui Yang, Yehao Lu, Yuning Su et al.· 0 citations