Hugging Face Trending Papers
TopoRig: Topology-Agnostic Facial Rigging via Multi-Source Supervision
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arXiv Computer Vision
Sep 15
TopoRig: Topology-Agnostic Facial Rigging via Multi-Source Supervision
arXiv:2609.15746v1 Announce Type: cross Abstract: Automatic facial rigging across heterogeneous mesh topologies remains challenging because high-quality expression supervision is often tied to canoni...
arXiv AI
Jul 21
Identity-Consistent Expression Fields: A Disentangled Neural Radiance Field Framework for Few-Shot Facial Expression Synthesis
arXiv:2607. 16287v1 Announce Type: cross Abstract: Neural Radiance Fields (NeRF) have enabled photorealistic novel-view synthesis of 3D scenes and, in the facial domain, have been extended to reconstruct and animate 3D face models from a small number of images.
arXiv Computer Vision
Aug 25
Multiple View Neural Regression of a Facial Shape Model
arXiv:2608.22655v1 Announce Type: new Abstract: Creating re-topologized 3D facial meshes is essential for high-quality facial animation but remains labor-intensive and time-consuming. This dissertati...
Hugging Face Trending Papers
Jul 30
ViP-Rig: Visual-Prompted Controllable Rigging
Rigging is inherently task-dependent because the same mesh may require different skeletons and deformation behaviors across animation tasks. In practice, artists often inspect an initial rig and repeatedly edit its skeletal structure and deformation behavior to meet specific animation requirements.
arXiv AI
Aug 12
X2C: A Large-Scale Benchmark for Nuanced Humanoid Facial Expression Imitation
arXiv:2505. 11146v4 Announce Type: replace-cross Abstract: Fine-grained facial expression transfer from humans to humanoid agents presents a unique pattern recognition challenge due to the significant domain gap between biological facial dynamics and mechanical control spaces.
arXiv Computer Vision
Sep 3
Make-It-Poseable: Feed-forward Latent Posing Model for 3D Characters
Make‑It‑Poseable is a feed‑forward framework that treats 3D character posing as a skinning‑free latent‑space transformation. It decouples shape deformation from fixed mesh connectivity, using a latent posing transformer, dense pose representation, and an adaptive completion module with bipartite‑matched latent loss. Experiments show it outperforms existing baselines, generalizes to varied morphologies, and supports 3D authoring tasks such as part replacement and refinement.