ZeroHSI: Zero-Shot 4D Human-Scene Interaction by Video Generation

• URL: http://arxiv.org/abs/2412.18600v1
• Date: Tue, 24 Dec 2024 18:55:38 GMT
• Title: ZeroHSI: Zero-Shot 4D Human-Scene Interaction by Video Generation
• Authors: Hongjie Li, Hong-Xing Yu, Jiaman Li, Jiajun Wu,
• Abstract summary: We present ZeroHSI, a novel approach that enables zero-shot 4D human-scene interaction synthesis by integrating video generation and neural human rendering. Our key insight is to leverage the rich motion priors learned by state-of-the-art video generation models, which have been trained on vast amounts of natural human movements and interactions, and use differentiable rendering to reconstruct human-scene interactions. We evaluate ZeroHSI on a curated dataset of different types of various indoor and outdoor scenes with different interaction prompts, demonstrating its ability to generate diverse and contextually appropriate human-scene interactions.
• Score: 17.438484695828276
• License:
• Abstract: Human-scene interaction (HSI) generation is crucial for applications in embodied AI, virtual reality, and robotics. While existing methods can synthesize realistic human motions in 3D scenes and generate plausible human-object interactions, they heavily rely on datasets containing paired 3D scene and motion capture data, which are expensive and time-consuming to collect across diverse environments and interactions. We present ZeroHSI, a novel approach that enables zero-shot 4D human-scene interaction synthesis by integrating video generation and neural human rendering. Our key insight is to leverage the rich motion priors learned by state-of-the-art video generation models, which have been trained on vast amounts of natural human movements and interactions, and use differentiable rendering to reconstruct human-scene interactions. ZeroHSI can synthesize realistic human motions in both static scenes and environments with dynamic objects, without requiring any ground-truth motion data. We evaluate ZeroHSI on a curated dataset of different types of various indoor and outdoor scenes with different interaction prompts, demonstrating its ability to generate diverse and contextually appropriate human-scene interactions.

Related papers

• Scaling Up Dynamic Human-Scene Interaction Modeling [58.032368564071895]
  TRUMANS is the most comprehensive motion-captured HSI dataset currently available. It intricately captures whole-body human motions and part-level object dynamics. We devise a diffusion-based autoregressive model that efficiently generates HSI sequences of any length.
  arXiv  Detail & Related papers  (2024-03-13T15:45:04Z)
• Revisit Human-Scene Interaction via Space Occupancy [55.67657438543008]
  Human-scene Interaction (HSI) generation is a challenging task and crucial for various downstream tasks. In this work, we argue that interaction with a scene is essentially interacting with the space occupancy of the scene from an abstract physical perspective. By treating pure motion sequences as records of humans interacting with invisible scene occupancy, we can aggregate motion-only data into a large-scale paired human-occupancy interaction database.
  arXiv  Detail & Related papers  (2023-12-05T12:03:00Z)
• GenZI: Zero-Shot 3D Human-Scene Interaction Generation [39.9039943099911]
  We propose GenZI, the first zero-shot approach to generating 3D human-scene interactions. Key to GenZI is our distillation of interaction priors from large vision-language models (VLMs), which have learned a rich semantic space of 2D human-scene compositions. In contrast to existing learning-based approaches, GenZI circumvents the conventional need for captured 3D interaction data.
  arXiv  Detail & Related papers  (2023-11-29T15:40:11Z)
• Synthesizing Diverse Human Motions in 3D Indoor Scenes [16.948649870341782]
  We present a novel method for populating 3D indoor scenes with virtual humans that can navigate in the environment and interact with objects in a realistic manner. Existing approaches rely on training sequences that contain captured human motions and the 3D scenes they interact with. We propose a reinforcement learning-based approach that enables virtual humans to navigate in 3D scenes and interact with objects realistically and autonomously.
  arXiv  Detail & Related papers  (2023-05-21T09:22:24Z)
• CIRCLE: Capture In Rich Contextual Environments [69.97976304918149]
  We propose a novel motion acquisition system in which the actor perceives and operates in a highly contextual virtual world. We present CIRCLE, a dataset containing 10 hours of full-body reaching motion from 5 subjects across nine scenes. We use this dataset to train a model that generates human motion conditioned on scene information.
  arXiv  Detail & Related papers  (2023-03-31T09:18:12Z)
• HUMANISE: Language-conditioned Human Motion Generation in 3D Scenes [54.61610144668777]
  We present a novel scene-and-language conditioned generative model that can produce 3D human motions in 3D scenes. Our experiments demonstrate that our model generates diverse and semantically consistent human motions in 3D scenes.
  arXiv  Detail & Related papers  (2022-10-18T10:14:11Z)
• Compositional Human-Scene Interaction Synthesis with Semantic Control [16.93177243590465]
  We aim to synthesize humans interacting with a given 3D scene controlled by high-level semantic specifications. We design a novel transformer-based generative model, in which the articulated 3D human body surface points and 3D objects are jointly encoded. Inspired by the compositional nature of interactions that humans can simultaneously interact with multiple objects, we define interaction semantics as the composition of varying numbers of atomic action-object pairs.
  arXiv  Detail & Related papers  (2022-07-26T11:37:44Z)
• HSPACE: Synthetic Parametric Humans Animated in Complex Environments [67.8628917474705]
  We build a large-scale photo-realistic dataset, Human-SPACE, of animated humans placed in complex indoor and outdoor environments. We combine a hundred diverse individuals of varying ages, gender, proportions, and ethnicity, with hundreds of motions and scenes, in order to generate an initial dataset of over 1 million frames. Assets are generated automatically, at scale, and are compatible with existing real time rendering and game engines.
  arXiv  Detail & Related papers  (2021-12-23T22:27:55Z)
• PLACE: Proximity Learning of Articulation and Contact in 3D Environments [70.50782687884839]
  We propose a novel interaction generation method, named PLACE, which explicitly models the proximity between the human body and the 3D scene around it. Our perceptual study shows that PLACE significantly improves the state-of-the-art method, approaching the realism of real human-scene interaction.
  arXiv  Detail & Related papers  (2020-08-12T21:00:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.