REVIEW 4 cited by
A Survey on Long Video Generation: Challenges, Methods, and Prospects
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Video generation is a rapidly advancing research area, garnering significant attention due to its broad range of applications. One critical aspect of this field is the generation of long-duration videos, which presents unique challenges and opportunities. This paper presents the first survey of recent advancements in long video generation and summarises them into two key paradigms: divide and conquer temporal autoregressive. We delve into the common models employed in each paradigm, including aspects of network design and conditioning techniques. Furthermore, we offer a comprehensive overview and classification of the datasets and evaluation metrics which are crucial for advancing long video generation research. Concluding with a summary of existing studies, we also discuss the emerging challenges and future directions in this dynamic field. We hope that this survey will serve as an essential reference for researchers and practitioners in the realm of long video generation.
Forward citations
Cited by 4 Pith papers
-
WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World
A five-aspect, 24-metric benchmark, a 26K human-annotated dataset, and an AI evaluator show that today's driving world models cannot simultaneously look real, respect geometry, and behave safely.
-
ERMV: Editing 4D Robotic Multi-view images to enhance embodied agents
ERMV edits 4D multi-view robot videos from one edited frame plus robot states, and VLA policies trained on the edited data show higher success rates in simulation and real robot tests.
-
ATLAS: Learning to Optimally Memorize the Context at Test Time
A new recurrent memory module (Atlas) that optimizes its memory over a window of past tokens and uses Muon-style second-order updates, reporting gains over Transformers and existing linear RNNs on long-context tasks.
-
Frame-Level Captions for Long Video Generation with Complex Multi Scenes
Frame-level captions with per-frame cross-attention and parallel multi-window denoising reduce semantic confusion in long multi-scene video generation in the authors' internal evaluation.
Discussion (0). Continue with ORCID to comment.