BLARM: Animating 3D Objects from Video via Blending Latent Rigid Motion Primitives
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
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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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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