arXiv Computer Vision By Pradyumn Goyal, Yizhak Ben-Shabat, Hsueh-Ti Derek Liu, Haomiao Jiang, Snehasish Mukherjee, Kyle Spence, Mark Stauber, Evangelos Kalogerakis, Yunze Zeng

BLARM: Animating 3D Objects from Video via Blending Latent Rigid Motion Primitives

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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.

arXiv AI
Jul 1

LUNA: Learning Universal 3D Human Animation Beyond Skinning

arXiv:2606. 31981v1 Announce Type: cross Abstract: Creating photorealistic, animatable 3D human avatars from monocular images still largely depends on Linear Blend Skinning (LBS) and parametric body models, which constrain expressivity and often introduce artifacts due to imperfect fitting.

By Peng Li, Rawal Khirodkar, Junxuan Li, Yuan Dong, Chen Cao, Yuan Liu, Wenhan Luo, Yike Guo, Shunsuke Saito
arXiv Computer Vision
Sep 3

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.

By Brian Nlong Zhao, Zhuoyang Pan, James M. Rehg, Jiajun Wu, Shangzhe Wu