1 citations · 1 across the 5 of their papers we have counts for
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BLARM: Animating 3D Objects from Video via Blending Latent Rigid Motion Primitives
Pradyumn Goyal, Yizhak Ben-Shabat, Hsueh-Ti Derek Liu +6
We introduce BLARM, a feed-forward method for video-driven 3D mesh animation. Given a monocular video and a static object mesh, BLARM predicts a temporally coherent animated mesh w…
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…
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…