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FoundHand is trained on our large-scale FoundHand-10M dataset which contains automatically extracted 2D keypoints and segmentation mask annotations (top left). FoundHand is formulated as a 2D pose-conditioned image-to-image diffusion model that enables precise hand pose and camera viewpoint control (top right). Optionally, we can condition the generation with a reference image to preserve its style (top right). Our model demonstrates exceptional in-the-wild generalization across hand-centric applications and has core capabilities. such as gesture transfer, domain transfer, and novel view synthesis (middle row). This endows FoundHand with zero-shot applications to fix malformed hand images and synthesize coherent hand and hand-object videos, without explicitly giving object cues (bottom row). 

Advances in neural fields are enablling high-fidelity capture of shape and appearance of dynamic 3D scenes. However, this capbabilities lag behind those offered by conventional representations such as 2D videos because of algorithmic challenges and the lack of large-scale multi-view real-world datasets. We address the dataset limitations with DiVa-360, a real-world 360° dynamic visual dataset that contains synchronized high-resolution and long-duration multi-view video sequences of table-scale scenes captured using a customized low-cost system with 53 cameras. It contains 21 object-centric sequences categorized by different motion types, 25 intricate hand-object interaction sequences, and 8 long-duration sequences for a total of 17.4M frames. In addition, we provide foreground-background segmentation masks, synchronized audio, and text descriptions. We benchmark the state-of-the-art dynamic neural field methods on DiVa-360 and provide insights about existing methods and future challenges on long-duration neural field capture.