WorldForge: Unlocking Emergent 3D/4D Generation in Video Diffusion Model
  via Training-Free Guidance
Authors: name: Chenxi Song, name: Yanming Yang, name: Tong Zhao, name: Ruibo Li, name: Chi Zhang

Recent video diffusion models demonstrate strong potential in spatial
intelligence tasks due to their rich latent world priors. However, this
potential is hindered by their limited controllability and geometric
inconsistency, creating a gap between their strong priors and their practical
use in 3D/4D tasks. As a result, current approaches often rely on retraining or
fine-tuning, which risks degrading pretrained knowledge and incurs high
computational costs. To address this, we propose WorldForge, a training-free,
inference-time framework composed of three tightly coupled modules. Intra-Step
Recursive Refinement introduces a recursive refinement mechanism during
inference, which repeatedly optimizes network predictions within each denoising
step to enable precise trajectory injection. Flow-Gated Latent Fusion leverages
optical flow similarity to decouple motion from appearance in the latent space
and selectively inject trajectory guidance into motion-related channels.
Dual-Path Self-Corrective Guidance compares guided and unguided denoising paths
to adaptively correct trajectory drift caused by noisy or misaligned structural
signals. Together, these components inject fine-grained, trajectory-aligned
guidance without training, achieving both accurate motion control and
photorealistic content generation. Extensive experiments across diverse
benchmarks validate our method's superiority in realism, trajectory
consistency, and visual fidelity. This work introduces a novel plug-and-play
paradigm for controllable video synthesis, offering a new perspective on
leveraging generative priors for spatial intelligence.