arXiv Computer Vision
ArtiMo: Agent-Driven Articulated Mesh Animation
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arXiv AI
6d ago
SMART: Zero-Shot Sim-to-Real Articulated Object Manipulation via Large-Scale Synthetic Pretraining
SMART introduces a scalable system for zero‑shot sim‑to‑real articulated object manipulation by leveraging large‑scale synthetic pretraining. The core component, SMART‑Sim, is an articulation‑aware simulation platform that facilitates efficient task generation and demonstration collection. Using SMART‑Sim, the authors synthesize SMART‑Data, comprising over 1 million demonstrations across 44 task types, 5 robot setups, and 2,507 articulated objects, and demonstrate that a vision‑language‑action model pretrained on this data achieves competitive simulation performance and zero‑shot transfer to real‑world tasks.
Hugging Face Trending Papers
Jul 9
SkelGen4D: Weakly-Supervised Skeleton-Based 4D Generation for Text-Driven Mesh Animation
We study 4D generation to synthesize temporally coherent sequences of 3D geometry for animation and content creation. In contrast to existing SDS-based optimization methods and video-driven animation approaches, we adopt a skeleton-driven animation framework aligned with standard industrial pipelines, which enables explicit control and editing.
arXiv Computer Vision
4d ago
Controllable Exaggeration for Generative Motion Models via Training-Time Adaptation and Inference-Time Guidance
The paper introduces a two-stage framework to incorporate the animation principle of exaggeration into motion generative models. During training, it fine‑tunes pre‑trained text‑to‑motion models on a curated exaggeration dataset. At inference, it uses a mathematical formulation based on dynamic movement primitives to guide diffusion and flow‑matching models toward more expressive, exaggerated motions while maintaining physical plausibility and the original motion intent.
arXiv Computer Vision
Sep 28
InternW0-$\Delta$: A World Action Model Bridging Predictive Dynamics and Actions with 20K+ Hours of Open Data
InternW0-Δ is a unified World Action Model that integrates pretrained visual dynamics, scene semantics, 4D geometry, and motion priors within a Mixture-of-Transformers framework to generate robot actions. It leverages a frozen VLM for semantic guidance, a 4D foundation model for geometric priors, and introduces Causal Imprint to learn future-relevant scene changes without future-video rollout. The model is pretrained on a newly curated 20K‑hour heterogeneous corpus of robot and human demonstrations, achieving superior performance on simulation benchmarks and real‑robot platforms.
arXiv Computer Vision
5d ago
BiPO: Bidirectional Partial Occlusion Network for Text-to-Motion Synthesis
arXiv:2412.00112v5 Announce Type: replace Abstract: Generating natural and expressive human motions from textual descriptions is challenging due to the complexity of coordinating full-body dynamics a...
arXiv AI
Jun 30
RoboGaze: Evaluating Robot World Models via Structured Vision-Language Analysis
arXiv:2606. 28385v1 Announce Type: cross Abstract: Recent advances in robot world models enable synthetic video generation for embodied prediction and planning.