$R^2$-Mesh: Reinforcement Learning Powered Mesh Reconstruction via Geometry and Appearance Refinement

Mesh reconstruction based on Neural Radiance Fields (NeRF) is popular in a variety of applications such as computer graphics, virtual reality, and medical imaging due to its efficiency in handling complex geometric structures and facilitating real-time rendering. However, existing works often fail to capture fine geometric details accurately and struggle with optimizing rendering quality. To address these challenges, we propose a novel algorithm that progressively generates and optimizes meshes from multi-view images. Our approach initiates with the training of a NeRF model to establish an initial Signed Distance Field (SDF) and a view-dependent appearance field. Subsequently, we iteratively refine the SDF through a differentiable mesh extraction method, continuously updating both the vertex positions and their connectivity based on the loss from mesh differentiable rasterization, while also optimizing the appearance representation. To further leverage high-fidelity and detail-rich representations from NeRF, we propose an online-learning strategy based on Upper Confidence Bound (UCB) to enhance viewpoints by adaptively incorporating images rendered by the initial NeRF model into the training dataset. Through extensive experiments, we demonstrate that our method delivers highly competitive and robust performance in both mesh rendering quality and geometric quality.

Promptus: Can Prompts Streaming Replace Video Streaming with Stable Diffusion

With the exponential growth of video traffic, traditional video streaming systems are approaching their limits in compression efficiency and communication capacity. To further reduce bitrate while maintaining quality, we propose Promptus, a disruptive novel system that streaming prompts instead of video content with Stable Diffusion, which converts video frames into a series of "prompts" for delivery. To ensure pixel alignment, a gradient descent-based prompt fitting framework is proposed. To achieve adaptive bitrate for prompts, a low-rank decomposition-based bitrate control algorithm is introduced. For inter-frame compression of prompts, a temporal smoothing-based prompt interpolation algorithm is proposed. Evaluations across various video domains and real network traces demonstrate Promptus can enhance the perceptual quality by 0.111 and 0.092 (in LPIPS) compared to VAE and H.265, respectively, and decreases the ratio of severely distorted frames by 89.3% and 91.7%. Moreover, Promptus achieves real-time video generation from prompts at over 150 FPS. To the best of our knowledge, Promptus is the first attempt to replace video codecs with prompt inversion and the first to use prompt streaming instead of video streaming. Our work opens up a new paradigm for efficient video communication beyond the Shannon limit.