CVJan 30, 2024

ContactGen: Contact-Guided Interactive 3D Human Generation for Partners

arXiv:2401.17212v25 citationsh-index: 4AAAI
Originality Incremental advance
AI Analysis

This work addresses the challenge of generating realistic 3D human interactions for applications in animation or virtual reality, but it is incremental as it builds on existing diffusion models for a specific domain.

The paper tackles the problem of generating interactive 3D human poses for a given partner human based on physical contact, proposing a method that uses a contact prediction module and guided diffusion to produce physically plausible and diverse poses, as demonstrated on the CHI3D dataset.

Among various interactions between humans, such as eye contact and gestures, physical interactions by contact can act as an essential moment in understanding human behaviors. Inspired by this fact, given a 3D partner human with the desired interaction label, we introduce a new task of 3D human generation in terms of physical contact. Unlike previous works of interacting with static objects or scenes, a given partner human can have diverse poses and different contact regions according to the type of interaction. To handle this challenge, we propose a novel method of generating interactive 3D humans for a given partner human based on a guided diffusion framework. Specifically, we newly present a contact prediction module that adaptively estimates potential contact regions between two input humans according to the interaction label. Using the estimated potential contact regions as complementary guidances, we dynamically enforce ContactGen to generate interactive 3D humans for a given partner human within a guided diffusion model. We demonstrate ContactGen on the CHI3D dataset, where our method generates physically plausible and diverse poses compared to comparison methods.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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