MindJourney: Test-Time Scaling with World Models for Spatial Reasoning
Paper • 2507.12508 • Published • 27
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import load_image, export_to_video
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("yyuncong/MindJourney-World-Model", dtype=torch.bfloat16, device_map="cuda")
pipe.to("cuda")
prompt = "A man with short gray hair plays a red electric guitar."
image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png"
)
output = pipe(image=image, prompt=prompt).frames[0]
export_to_video(output, "output.mp4")MindJourney: Test-Time Scaling with World Models for Spatial Reasoning
NeurIPS 2025
Yuncong Yang, Jiageng Liu, Zheyuan Zhang, Siyuan Zhou, Reuben Tan, Jianwei Yang, Yilun Du, Chuang Gan
MindJourney is a test-time scaling framework that leverages the 3D imagination capability of World Models to strengthen spatial reasoning in Vision-Language Models (VLMs). We evaluate on the SAT dataset and provide a baseline pipeline, a Stable Virtual Camera (SVC) based spatial beam search pipeline, and a Search World Model (SWM) based spatial beam search pipeline.
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