CVPR 2024 Papers

In this paper we present RStab a novel framework for video stabilization that integrates 3D multi-frame fusion through volume rendering. Departing from conventional methods we introduce a 3D multi-frame perspective to generate stabilized images addressing the challenge of full-frame generation while preserving structure. The core of our RStab framework lies in Stabilized Rendering (SR) a volume rendering module fusing multi-frame information in 3D space. Specifically SR involves warping features and colors from multiple frames by projection fusing them into descriptors to render the stabilized image. However the precision of warped information depends on the projection accuracy a factor significantly influenced by dynamic regions. In response we introduce the Adaptive Ray Range (ARR) module to integrate depth priors adaptively defining the sampling range for the projection process. Additionally we propose Color Correction (CC) assisting geometric constraints with optical flow for accurate color aggregation. Thanks to the three modules our RStab demonstrates superior performance compared with previous stabilizers in the field of view (FOV) image quality and video stability across various datasets.

When working with 3D facial data improving fidelity and avoiding the uncanny valley effect is critically dependent on accurate 3D facial performance capture. Because such methods are expensive and due to the widespread availability of 2D videos recent methods have focused on how to perform monocular 3D face tracking. However these methods often fall short in capturing precise facial movements due to limitations in their network architecture training and evaluation processes. Addressing these challenges we propose a novel face tracker FlowFace that introduces an innovative 2D alignment network for dense per-vertex alignment. Unlike prior work FlowFace is trained on high-quality 3D scan annotations rather than weak supervision or synthetic data. Our 3D model fitting module jointly fits a 3D face model from one or many observations integrating existing neutral shape priors for enhanced identity and expression disentanglement and per-vertex deformations for detailed facial feature reconstruction. Additionally we propose a novel metric and benchmark for assessing tracking accuracy. Our method exhibits superior performance on both custom and publicly available benchmarks. We further validate the effectiveness of our tracker by generating high-quality 3D data from 2D videos which leads to performance gains on downstream tasks.

Human perception of the world is shaped by a multitude of viewpoints and modalities. While many existing datasets focus on scene understanding from a certain perspective (e.g. egocentric or third-person views) our dataset offers a panoptic perspective (i.e. multiple viewpoints with multiple data modalities). Specifically we encapsulate third-person panoramic and front views as well as egocentric monocular/binocular views with rich modalities including video multi-channel audio directional binaural delay location data and textual scene descriptions within each scene captured presenting comprehensive observation of the world. To the best of our knowledge this is the first database that covers multiple viewpoints with multiple data modalities to mimic how daily information is accessed in the real world. Through our benchmark analysis we presented 5 different scene understanding tasks on the proposed 360+x dataset to evaluate the impact and benefit of each data modality and perspective in panoptic scene understanding. We hope this unique dataset could broaden the scope of comprehensive scene understanding and encourage the community to approach these problems from more diverse perspectives.