6 papers
A Geodesic Cut-Cell Prior for Neural Skinning
Wenchao Ma, Surya Dwarakanath, Yizhak Ben-Shabat +4
We introduce cut-cell skinning, a geometric prior designed to augment data-driven skinning weight generation. While data-driven methods show promise in producing high-quality skinn…
RoMo: A Large-Scale, Richly Organized Dataset and Semantic Taxonomy for Human Motion Generation
Jiahao Zhang, Joseph Liu, Young-Yoon Lee +9
Success in generative modeling across language, image, and video demonstrates that large, well-curated datasets are the key driver for building capable models. 3D Human motion, how…
VI3NR: Variance Informed Initialization for Implicit Neural Representations
Chamin Hewa Koneputugodage, Yizhak Ben-Shabat, Sameera Ramasinghe +1
Implicit Neural Representations (INRs) are a versatile and powerful tool for encoding various forms of data, including images, videos, sound, and 3D shapes. A critical factor in th…
GEOPARD: Geometric Pretraining for Articulation Prediction in 3D Shapes
Pradyumn Goyal, Dmitry Petrov, Sheldon Andrews +3
We present GEOPARD, a transformer-based architecture for predicting articulation from a single static snapshot of a 3D shape. The key idea of our method is a pretraining strategy t…
StyleMotif: Multi-Modal Motion Stylization using Style-Content Cross Fusion
Ziyu Guo, Young Yoon Lee, Joseph Liu +3
We present StyleMotif, a novel Stylized Motion Latent Diffusion model, generating motion conditioned on both content and style from multiple modalities. Unlike existing approaches…
Neural Experts: Mixture of Experts for Implicit Neural Representations
Yizhak Ben-Shabat, Chamin Hewa Koneputugodage, Sameera Ramasinghe +1
Implicit neural representations (INRs) have proven effective in various tasks including image, shape, audio, and video reconstruction. These INRs typically learn the implicit field…