Yume: An Interactive World Generation Model
Authors: name: Xiaofeng Mao, name: Shaoheng Lin, name: Zhen Li, name: Chuanhao Li, name: Wenshuo Peng, name: Tong He, name: Jiangmiao Pang, name: Mingmin Chi, name: Yu Qiao, name: Kaipeng Zhang

Yume aims to use images, text, or videos to create an interactive, realistic,
and dynamic world, which allows exploration and control using peripheral
devices or neural signals. In this report, we present a preview version of
\method, which creates a dynamic world from an input image and allows
exploration of the world using keyboard actions. To achieve this high-fidelity
and interactive video world generation, we introduce a well-designed framework,
which consists of four main components, including camera motion quantization,
video generation architecture, advanced sampler, and model acceleration. First,
we quantize camera motions for stable training and user-friendly interaction
using keyboard inputs. Then, we introduce the Masked Video Diffusion
Transformer~(MVDT) with a memory module for infinite video generation in an
autoregressive manner. After that, training-free Anti-Artifact Mechanism (AAM)
and Time Travel Sampling based on Stochastic Differential Equations (TTS-SDE)
are introduced to the sampler for better visual quality and more precise
control. Moreover, we investigate model acceleration by synergistic
optimization of adversarial distillation and caching mechanisms. We use the
high-quality world exploration dataset \sekai to train \method, and it achieves
remarkable results in diverse scenes and applications. All data, codebase, and
model weights are available on https://github.com/stdstu12/YUME. Yume will
update monthly to achieve its original goal. Project page:
https://stdstu12.github.io/YUME-Project/.