Endless World: Real-Time 3D-Aware Long Video Generation

• URL: http://arxiv.org/abs/2512.12430v1
• Date: Sat, 13 Dec 2025 19:06:12 GMT
• Title: Endless World: Real-Time 3D-Aware Long Video Generation
• Abstract summary: Endless World is a real-time framework for infinite, 3D-consistent video generation.<n>We introduce a conditional autoregressive training strategy that aligns newly generated content with existing video frames.<n>Our 3D injection mechanism enforces physical plausibility and geometric consistency throughout extended sequences.
• Score: 57.411689597435334
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Producing long, coherent video sequences with stable 3D structure remains a major challenge, particularly in streaming scenarios. Motivated by this, we introduce Endless World, a real-time framework for infinite, 3D-consistent video generation.To support infinite video generation, we introduce a conditional autoregressive training strategy that aligns newly generated content with existing video frames. This design preserves long-range dependencies while remaining computationally efficient, enabling real-time inference on a single GPU without additional training overhead.Moreover, our Endless World integrates global 3D-aware attention to provide continuous geometric guidance across time. Our 3D injection mechanism enforces physical plausibility and geometric consistency throughout extended sequences, addressing key challenges in long-horizon and dynamic scene synthesis.Extensive experiments demonstrate that Endless World produces long, stable, and visually coherent videos, achieving competitive or superior performance to existing methods in both visual fidelity and spatial consistency. Our project has been available on https://bwgzk-keke.github.io/EndlessWorld/.

Related papers

• Lyra 2.0: Explorable Generative 3D Worlds [77.45279013687427]
  We present Lyra 2.0, a framework for generating persistent, explorable 3D worlds at scale.<n>To address spatial forgetting, we maintain per-frame 3D geometry and use it solely for information routing.<n>We train with self-augmented histories that expose the model to its own degraded outputs, teaching it to correct drift rather than propagate it.
  arXiv  Detail & Related papers  (2026-04-14T17:59:44Z)
• Beyond Pixel Histories: World Models with Persistent 3D State [50.4601060508243]
  PERSIST is a new paradigm of world model which simulates the evolution of a latent 3D scene.<n>We show substantial improvements in spatial memory, 3D consistency, and long-horizon stability over existing methods.
  arXiv  Detail & Related papers  (2026-03-03T19:58:31Z)
• InfiniteVGGT: Visual Geometry Grounded Transformer for Endless Streams [22.277387867568834]
  InfiniteVGGT is a causal visual geometry transformer that operationalizes the concept of a rolling memory through a bounded yet adaptive and perpetually expressive KV cache.<n>We introduce the Long3D benchmark, which, for the first time, enables a rigorous evaluation of continuous 3D geometry estimation on sequences about 10,000 frames.
  arXiv  Detail & Related papers  (2026-01-05T17:11:00Z)
• WorldWarp: Propagating 3D Geometry with Asynchronous Video Diffusion [78.20778143251171]
  WorldWarp is a framework that couples a 3D structural anchor with a 2D generative refiner.<n>WorldWarp maintains consistency across video chunks by dynamically updating the 3D cache at every step.<n>It achieves state-of-the-art fidelity by ensuring that 3D logic guides structure while diffusion logic perfects texture.
  arXiv  Detail & Related papers  (2025-12-22T18:53:50Z)
• LongVie 2: Multimodal Controllable Ultra-Long Video World Model [94.85126937285527]
  LongVie 2 is an end-to-end autoregressive framework trained in three stages.<n>LongVie 2 achieves state-of-the-art performance in long-range controllability, temporal coherence, and visual fidelity.
  arXiv  Detail & Related papers  (2025-12-15T17:59:58Z)
• StereoWorld: Geometry-Aware Monocular-to-Stereo Video Generation [108.97993219426509]
  StereoWorld is an end-to-end framework for high-fidelity monocular-to-stereo video generation.<n>Our framework conditions the model on the monocular video input while explicitly supervising the generation with a geometry-aware regularization.<n>To enable large-scale training and evaluation, we curate a high-definition stereo video dataset.
  arXiv  Detail & Related papers  (2025-12-10T06:50:16Z)
• FantasyWorld: Geometry-Consistent World Modeling via Unified Video and 3D Prediction [13.098585993121722]
  We present FantasyWorld, a geometry-enhanced framework that augments frozen video foundation models with a trainable geometric branch.<n>Our approach introduces cross-branch supervision, where geometry cues guide video generation and video priors regularize 3D prediction.<n>Experiments show that FantasyWorld effectively bridges video imagination and 3D perception, outperforming recent geometry-consistent baselines in multi-view coherence and style consistency.
  arXiv  Detail & Related papers  (2025-09-25T22:24:23Z)
• Voyager: Long-Range and World-Consistent Video Diffusion for Explorable 3D Scene Generation [66.95956271144982]
  We present Voyager, a novel video diffusion framework that generates world-consistent 3D point-cloud sequences from a single image.<n>Unlike existing approaches, Voyager achieves end-to-end scene generation and reconstruction with inherent consistency across frames.
  arXiv  Detail & Related papers  (2025-06-04T17:59:04Z)
• Seeing World Dynamics in a Nutshell [132.79736435144403]
  NutWorld is a framework that transforms monocular videos into dynamic 3D representations in a single forward pass.<n>We demonstrate that NutWorld achieves high-fidelity video reconstruction quality while enabling downstream applications in real-time.
  arXiv  Detail & Related papers  (2025-02-05T18:59:52Z)
• World-Consistent Video-to-Video Synthesis [35.617437747886484]
  We introduce a novel vid2vid framework that efficiently utilizes all past generated frames during rendering. This is achieved by condensing the 3D world rendered so far into a physically-grounded estimate of the current frame. We propose a novel neural network architecture to take advantage of the information stored in the guidance images.
  arXiv  Detail & Related papers  (2020-07-16T17:58:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.