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arXiv Computer Vision

Kirin: Animal Motion Generation from In-the-Wild Video

Kirin is a new framework that reconstructs 3D animal motion from in‑the‑wild videos, learns motion priors at scale, and generates realistic motion conditioned on text and image. It introduces AiM3D, the first large‑scale dataset of aligned video‑text‑motion tuples for quadruped animals, and uses an off‑the‑shelf image‑to‑3D model to automatically rig and animate 3D meshes with the generated motion. The framework and dataset provide a foundation for large‑scale, text and image‑conditioned animal motion generation and animation.

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UniMo: Unifying Human and Animal Motion Generation

UniMo introduces a unified point‑cloud based framework for generating 3D motion that works for both humans and animals, overcoming challenges posed by diverse skeletal topologies and limited animal datasets. It converts parametric skeletons into unparametric representations and uses dynamic sampling to focus on active joints. The authors also release UniML3D, a large motion‑language dataset with 145,907 sequences and 433,388 captions, and demonstrate state‑of‑the‑art performance on multiple benchmarks.

By Zeyu Zhang, Zhiyuan Zhang, Siheng Wang, Yiran Wang, Danning Li, Ian Reid, Richard Hartley
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ORMA: Optimization-based Monocular 4D Reconstruction of Articulated Animals

ORMA is a training‑free framework that reconstructs articulated 4D representations of animals from monocular videos by decoupling pose and shape. It uses predicted pose as a reference for optimization and generative 3D priors to refine shape, aligning the result with the SMAL+ parametric model. The method combines per‑frame pose estimates with globally consistent camera poses, and further refines the reconstruction using self‑supervised DINO correspondences and temporal consistency, achieving improved accuracy on the new PAW4D benchmark and diverse real‑world videos.

By Xuyi Hu, Francesco Palandra, Shangzhe Wu, Daniel Cremers, Riccardo Marin, Silvia Zuffi
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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.

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arXiv AI
Oct 9

SAM 3D Animal: Promptable Animal 3D Reconstruction from Images in the Wild

SAM 3D Animal is a promptable framework that performs multi‑animal 3D reconstruction from a single image, using the SMAL+ parametric animal model. It jointly reconstructs multiple instances and allows flexible prompts such as keypoints and masks to better handle crowded and occluded scenes. The authors also introduce Herd3D, a new dataset of over 5,000 images that increases species, interaction, and occlusion diversity, and demonstrate state‑of‑the‑art performance on several benchmark datasets.

By Xuyi Hu, Jin Lyu, Jiuming Liu, Yebin Liu, Silvia Zuffi, Liang An, Stefan Goetz
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arXiv Computer Vision
4d ago

MolmoMotion: Forecasting Point Trajectories in 3D with Language Instruction

arXiv:2606.18558v2 Announce Type: replace Abstract: Motion forecasting is central to visual intelligence: agents must anticipate how objects will move in order to plan actions, reason about physical...

By Jianing Zhang, Chenhao Zheng, Yajun Yang, Max Argus, Rustin Soraki, Winson Han, Taira Anderson, Chun-Liang Li, Shuo Liu, Jiafei Duan, Zhongzheng Ren, Jieyu Zhang, Ranjay Krishna
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