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Found 4 papers, 1 papers with code
MoManifold: Learning to Measure 3D Human Motion via Decoupled Joint Acceleration Manifolds
Extensive experiments demonstrate that MoManifold outperforms existing SOTAs as a prior in several downstream tasks such as denoising real-world human mocap data, recovering human motion from partial 3D observations, mitigating jitters for SMPL-based pose estimators, and refining the results of motion in-betweening.
SINE: Semantic-driven Image-based NeRF Editing with Prior-guided Editing Field
Despite the great success in 2D editing using user-friendly tools, such as Photoshop, semantic strokes, or even text prompts, similar capabilities in 3D areas are still limited, either relying on 3D modeling skills or allowing editing within only a few categories.
CompNVS: Novel View Synthesis with Scene Completion
We introduce a scalable framework for novel view synthesis from RGB-D images with largely incomplete scene coverage.
LatentHuman: Shape-and-Pose Disentangled Latent Representation for Human Bodies
In this work, we propose a novel neural implicit representation for the human body, which is fully differentiable and optimizable with disentangled shape and pose latent spaces.
