Animal Avatars: Reconstructing Animatable 3D Animals from Casual Videos

• URL: http://arxiv.org/abs/2403.17103v1
• Date: Mon, 25 Mar 2024 18:41:43 GMT
• Title: Animal Avatars: Reconstructing Animatable 3D Animals from Casual Videos
• Authors: Remy Sabathier, Niloy J. Mitra, David Novotny,
• Abstract summary: We present a method to build animatable dog avatars from monocular videos. This is challenging as animals display a range of (unpredictable) non-rigid movements and have a variety of appearance details. We develop an approach that links the video frames via a 4D solution that jointly solves for animal's pose variation, and its appearance.
• Score: 26.65191922949358
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a method to build animatable dog avatars from monocular videos. This is challenging as animals display a range of (unpredictable) non-rigid movements and have a variety of appearance details (e.g., fur, spots, tails). We develop an approach that links the video frames via a 4D solution that jointly solves for animal's pose variation, and its appearance (in a canonical pose). To this end, we significantly improve the quality of template-based shape fitting by endowing the SMAL parametric model with Continuous Surface Embeddings, which brings image-to-mesh reprojection constaints that are denser, and thus stronger, than the previously used sparse semantic keypoint correspondences. To model appearance, we propose an implicit duplex-mesh texture that is defined in the canonical pose, but can be deformed using SMAL pose coefficients and later rendered to enforce a photometric compatibility with the input video frames. On the challenging CoP3D and APTv2 datasets, we demonstrate superior results (both in terms of pose estimates and predicted appearance) to existing template-free (RAC) and template-based approaches (BARC, BITE).

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