arXiv subjects

Bhavya Ramakrishna

Publications and source records attributed to Bhavya Ramakrishna.

2 recordsLinked to original sources

arXiv2026

ReVo: A Cross-Layer Reliable Volumetric Videoconferencing System

Volumetric videoconferencing enables immersive six Degrees of Freedom interactions by jointly transmitting visual appearance and 3D geometry. However, delivering volumetric video over today's networks remains challenging due to high bandwidth demands, strict real-time latency constraints, and frequent packet loss. Packet loss not only degrades visual quality but also corrupts geometric structure, leading to severe artifacts and video freezes that significantly degrade Quality of Experience. Existing solutions either optimize volumetric videos assuming reliable networks or focus on loss recovery for 2D video, and are insufficient for volumetric videoconferencing. In this paper, we present ReVo, a loss-resilient volumetric videoconferencing system that jointly recovers RGB and depth content under packet loss while meeting real-time constraints on desktop-grade hardware. ReVo leverages the insight that effective recovery requires a cross-layer, modality-aware design. It decouples volumetric video into RGB and depth streams, selectively protects critical content using network-layer FEC, and reconstructs corrupted non-critical frames using a post-decode neural recovery module. ReVo is implemented end-to-end over WebRTC and supports both traditional and neural video codecs. Our evaluations using real-world loss traces show that ReVo improves median SSIM by up to 32% (resp. 13%) for RGB (resp. depth) content and reduces video freezes by up to 95.7% compared to existing techniques.

cs.NI↗

arXiv2026

Audio-Visual Continual Test-Time Adaptation without Forgetting

Audio-visual continual test-time adaptation involves continually adapting a source audio-visual model at test-time, to unlabeled non-stationary domains, where either or both modalities can be distributionally shifted, which hampers online cross-modal learning and eventually leads to poor accuracy. While previous works have tackled this problem, we find that SOTA methods suffer from catastrophic forgetting where the model's performance drops well below even the source model due to continual parameter updates at test-time. In this work, we first show that adapting only the modality fusion layer to a target domain not only improves performance on that domain but can also enhance performance on subsequent domains. Based on this strong cross-task transferability of the fusion layer's parameters, we propose a method, $\texttt{AVReCAP}$, that improves test-time performance of the models without access to any source data. Our approach works by using a selective parameter retrieval mechanism that dynamically retrieves the best fusion layer parameters from a buffer using only a small batch of test data. These parameters are then integrated into the model, adapted to the current test distribution, and saved back for future use. Extensive experiments on benchmark datasets involving unimodal and bimodal corruptions show our proposed $\texttt{AVReCAP}$ significantly outperforms existing methods while minimizing catastrophic forgetting.

cs.LG↗