arXiv Computer Vision
SolarRenderer: Few-Step Generative Reference Rendering
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arXiv Computer Vision
Sep 3
SolarWM: Open Data and Scalable Training for Long-Horizon Video World Models
SolarWM is an open foundation for building interactive video world models, offering a reconfigurable multi‑source data engine that unifies 1.43 million clips from 10 datasets into a consistent, frame‑aligned format. It provides a backbone‑native adaptation framework that preserves native representations of models ranging from 5 B to 33 B parameters, and a three‑stage training recipe combining bidirectional adaptation, teacher‑forced autoregressive initialization, and distribution‑matching distillation. The resulting causal models can interact in real‑time over rollouts from minutes to hours, trained only on 5‑second sequences, and the project releases data, pipeline, recipes, weights, and framework for reproducible research.
arXiv Computer Vision
Oct 23
AnyGameRenderer: Entity-Consistent Generative Rendering Across Games and an Entity-Level Benchmark
AnyGameRenderer is a video‑to‑video generative renderer that transforms low‑quality, AI‑generated game footage—such as gray blockouts and colored primitives—into high‑quality visuals with detailed characters, motion, and effects while preserving layout, camera, and entity positions. It works across diverse game genres (tower defense, auto‑battlers, top‑down survivors, turn‑based battles, kart racing, crowd runners) and accepts plain video inputs without requiring complex engine data. Trained on ~10k paired clips from agent‑made games, Unreal Engine simulations, and real gameplay, it improves entity correspondence, raising temporal match from 50.9% to 62.9% and reducing missed count changes from 34.7% to 21.3%.
arXiv Computer Vision
Aug 24
Prompt2Effect: Training-Free Image-to-Video Model Specialization via LoRA Generation
arXiv:2606.13971v3 Announce Type: replace Abstract: While personalizing Image-to-Video (I2V) diffusion models with specific visual effects is increasingly demanded for high-end generation, current pr...
arXiv Computer Vision
Sep 4
Building Pretraining Data for World Models: An Unreal Engine-Based Pipeline for Action-Conditioned Video Generation
The paper introduces a large‑scale synthetic data pipeline built on Unreal Engine to generate action‑conditioned, multi‑view video for training world models. The system operates in two stages: real‑time physics simulation records trajectories, then offline rendering produces high‑quality video. It includes a distributed production framework with task partitioning, automated filtering, and a 25‑server cluster, yielding over 2,600 hours of 1080p and 6,000 hours of 720p video from 429 levels and 40 characters.
arXiv Computer Vision
Sep 25
ViRDM: Taming Representation Distribution Matching for Few-Step Causal Video Generation
ViRDM is a new post‑training method for few‑step causal video generation that eliminates the need for a large teacher model and an online critic. By applying representation distribution matching (RDM) with a precomputed target distribution, a lightweight VAE decoder, and staged vector–Jacobian products, ViRDM overcomes memory, optimization, and temporal dynamics challenges. The approach reduces GPU memory usage and training time, achieving state‑of‑the‑art VBench performance with only 20 generator updates and 16 A100 GPU‑hours.
arXiv AI
Aug 11
VideoCoCo: Code-as-CoT for Physically-Consistent Video Generation via an Agentic Dual-Engine System
arXiv:2607. 27380v2 Announce Type: replace-cross Abstract: Text-to-video models have achieved remarkable visual quality, yet they still struggle to generate physically consistent dynamics because the temporal evolution of a scene must be inferred implicitly from a highly compressed text prompt.