Shengze Wang

Shengze Wang, Alexey Supikov, Joshua Ratcliff et al.

Recent works in spatiotemporal radiance fields can produce photorealistic free-viewpoint videos. However, they are inherently unsuitable for interactive streaming scenarios (e.g. video conferencing, telepresence) because have an inevitable lag even if the training is instantaneous. This is because these approaches consume videos and thus have to buffer chunks of frames (often seconds) before processing. In this work, we take a step towards interactive streaming via a frame-by-frame approach naturally free of lag. Conventional wisdom believes that per-frame NeRFs are impractical due to prohibitive training costs and storage. We break this belief by introducing Incremental Neural Videos (INV), a per-frame NeRF that is efficiently trained and streamable. We designed INV based on two insights: (1) Our main finding is that MLPs naturally partition themselves into Structure and Color Layers, which store structural and color/texture information respectively. (2) We leverage this property to retain and improve upon knowledge from previous frames, thus amortizing training across frames and reducing redundant learning. As a result, with negligible changes to NeRF, INV can achieve good qualities (>28.6db) in 8min/frame. It can also outperform prior SOTA in 19% less training time. Additionally, our Temporal Weight Compression reduces the per-frame size to 0.3MB/frame (6.6% of NeRF). More importantly, INV is free from buffer lag and is naturally fit for streaming. While this work does not achieve real-time training, it shows that incremental approaches like INV present new possibilities in interactive 3D streaming. Moreover, our discovery of natural information partition leads to a better understanding and manipulation of MLPs. Code and dataset will be released soon.

Shengze Wang, Ziheng Wang, Ryan Schmelzle et al.

In this paper, we work to bring telepresence to every desktop. Unlike commercial systems, personal 3D video conferencing systems must render high-quality videos while remaining financially and computationally viable for the average consumer. To this end, we introduce a capturing and rendering system that only requires 4 consumer-grade RGBD cameras and synthesizes high-quality free-viewpoint videos of users as well as their environments. Experimental results show that our system renders high-quality free-viewpoint videos without using object templates or heavy pre-processing. While not real-time, our system is fast and does not require per-video optimizations. Moreover, our system is robust to complex hand gestures and clothing, and it can generalize to new users. This work provides a strong basis for further optimization, and it will help bring telepresence to every desk in the near future. The code and dataset will be made available on our website https://mcmvmc.github.io/PersonalTelepresence/.

Luchao Qi, Jiaye Wu, Annie N. Wang et al.

In recent years, generative 3D face models (e.g., EG3D) have been developed to tackle the problem of synthesizing photo-realistic faces. However, these models are often unable to capture facial features unique to each individual, highlighting the importance of personalization. Some prior works have shown promise in personalizing generative face models, but these studies primarily focus on 2D settings. Also, these methods require both fine-tuning and storing a large number of parameters for each user, posing a hindrance to achieving scalable personalization. Another challenge of personalization is the limited number of training images available for each individual, which often leads to overfitting when using full fine-tuning methods. Our proposed approach, My3DGen, generates a personalized 3D prior of an individual using as few as 50 training images. My3DGen allows for novel view synthesis, semantic editing of a given face (e.g. adding a smile), and synthesizing novel appearances, all while preserving the original person's identity. We decouple the 3D facial features into global features and personalized features by freezing the pre-trained EG3D and training additional personalized weights through low-rank decomposition. As a result, My3DGen introduces only $\textbf{240K}$ personalized parameters per individual, leading to a $\textbf{127}\times$ reduction in trainable parameters compared to the $\textbf{30.6M}$ required for fine-tuning the entire parameter space. Despite this significant reduction in storage, our model preserves identity features without compromising the quality of downstream applications.

Fei Fang, Yifan Hua, Shengze Wang et al.

While significant progress has been made in research and development on open-source and cost-efficient large-language models (LLMs), serving scalability remains a critical challenge, particularly for small organizations and individuals seeking to deploy and test their LLM innovations. Inspired by peer-to-peer networks that leverage decentralized overlay nodes to increase throughput and availability, we propose GenTorrent, an LLM serving overlay that harnesses computing resources from decentralized contributors. We identify four key research problems inherent to enabling such a decentralized infrastructure: 1) overlay network organization; 2) LLM communication privacy; 3) overlay forwarding for resource efficiency; and 4) verification of serving quality. This work presents the first systematic study of these fundamental problems in the context of decentralized LLM serving. Evaluation results from a prototype implemented on a set of decentralized nodes demonstrate that GenTorrent achieves a latency reduction of over 50% compared to the baseline design without overlay forwarding. Furthermore, the security features introduce minimal overhead to serving latency and throughput. We believe this work pioneers a new direction for democratizing and scaling future AI serving capabilities.