With the rapid development of high-speed communication and artificial intelligence
technologies, human perception of real-world scenes is no longer limited to the use of small …

Deep learning technique, has made tremendous progress in fluid mechanics in recent
years, because of its mighty feature extraction capacity from complicated and massive fluid …

We introduce CoTracker, a transformer-based model that tracks a large number of 2D points
in long video sequences. Differently from most existing approaches that track points …

We present Stable Video Diffusion-a latent video diffusion model for high-resolution, state-of-
the-art text-to-video and image-to-video generation. Recently, latent diffusion models trained …

Image diffusion models, trained on massive image collections, have emerged as the most
versatile image generator model in terms of quality and diversity. They support inverting real …

The pursuit of controllability as a higher standard of visual content creation has yielded
remarkable progress in customizable image synthesis. However, achieving controllable …

Abstract Recurrent All-Pairs Field Transforms (RAFT) has shown great potentials in
matching tasks. However, all-pairs correlations lack non-local geometry knowledge and …

Synthesizing visual content that meets users' needs often requires flexible and precise
controllability of the pose, shape, expression, and layout of the generated objects. Existing …

We present a new test-time optimization method for estimating dense and long-range motion
from a video sequence. Prior optical flow or particle video tracking algorithms typically …

We extend neural radiance fields (NeRFs) to dynamic large-scale urban scenes. Prior work
tends to reconstruct single video clips of short durations (up to 10 seconds). Two reasons …