HOSNeRF: Dynamic Human-Object-Scene Neural Radiance Fields from a Single Video (2304.12281v1)

Abstract: We introduce HOSNeRF, a novel 360{\deg} free-viewpoint rendering method that reconstructs neural radiance fields for dynamic human-object-scene from a single monocular in-the-wild video. Our method enables pausing the video at any frame and rendering all scene details (dynamic humans, objects, and backgrounds) from arbitrary viewpoints. The first challenge in this task is the complex object motions in human-object interactions, which we tackle by introducing the new object bones into the conventional human skeleton hierarchy to effectively estimate large object deformations in our dynamic human-object model. The second challenge is that humans interact with different objects at different times, for which we introduce two new learnable object state embeddings that can be used as conditions for learning our human-object representation and scene representation, respectively. Extensive experiments show that HOSNeRF significantly outperforms SOTA approaches on two challenging datasets by a large margin of 40% ~ 50% in terms of LPIPS. The code, data, and compelling examples of 360{\deg} free-viewpoint renderings from single videos will be released in https://showlab.github.io/HOSNeRF.

• The paper introduces a neural radiance field framework that integrates object bones to capture dynamic human-object interactions from a single video.
• It employs state-conditional embeddings to adapt the model to varying object motions and complex deformations during interactions.
• The method outperforms current techniques with a 40-50% improvement in perceptual image similarity, advancing VR, AR, and 3D animation applications.

Overview of HOSNeRF: Dynamic Human-Object-Scene Neural Radiance Fields from a Single Video

The paper "HOSNeRF: Dynamic Human-Object-Scene Neural Radiance Fields from a Single Video" introduces an innovative method for 360-degree free-viewpoint rendering of dynamic scenes involving human-object interactions from a singular, monocular video. The primary contribution of the work is the development of a neural radiance field framework capable of accurately reconstructing scenes with significant human-object interactions.

Key Contributions and Methodology

HOSNeRF addresses two major challenges in dynamic scene reconstruction: complex object motions and varying object interactions over time.

1. Object Bones Integration: The paper innovatively incorporates "object bones" into the traditional human skeleton hierarchy. This integration aids in accurately estimating large deformations in objects due to human interactions, such as carrying a suitcase or swinging a tennis racket. The object bones, paired with an object's linear blend skinning (LBS), enable precise articulation of motion relative to the corresponding human joint movements.
2. State-Conditional Representation: Recognizing that humans interact with different objects at different times, the authors introduce learnable object state embeddings. These embeddings condition the learning processes for both human-object and scene representations, allowing the model to dynamically adjust based on varying object interactions and transitions.
3. Training Strategy and Model Performance: The approach involves a three-stage training process optimized with a variety of losses including perceptual losses and cycle consistency to ensure accurate deformation mappings. In comparative evaluations, HOSNeRF demonstrates superior performance, significantly outperforming current state-of-the-art techniques in LPIPS—a perceptual metric for image similarity—by an impressive margin of 40% to 50%.

Implications and Future Directions

The implications of HOSNeRF's contributions are substantial in fields such as virtual reality (VR), augmented reality (AR), and 3D animation, where accurately rendering interactions between humans and objects in dynamic environments is crucial. The ability to produce immersive experiences from minimal input—a single video—offers substantial computational and practical advantages. As the researchers address the current limitations of handling dynamic backgrounds, the approach has the potential to further advance applications in fields that demand high-fidelity scene reconstructions.

In theoretical terms, the integration of object bones and conditionally dependent embeddings provides a compelling direction for neural radiance fields, moving beyond static or rigid models to capture the nuanced interactions between multiple dynamic elements. A potential avenue for future work could include expanding capabilities to involve interactions with multiple human subjects and further addressing dynamic environmental backgrounds within scenes.

In conclusion, HOSNeRF represents a significant step towards comprehensive scene understanding and reconstruction from limited data. By effectively modeling complex interactions and object states, this work lays a foundation for future exploration and innovation in creating more integrated and realistic virtual environments.