WorldForge: Unlocking Emergent 3D/4D Generation in Video Diffusion Model
  via Training-Free Guidance
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.
68ccb5de3df9ac65e93dc5fc
Chenxi Song
admin_assigned
2025-09-19T10:40:23.503Z
65cf975bf5a15aa42146b208
/avatars/d62e3907331a24bb899b7a1fdf4299e7.svg
Song
user
ChenxiSong
68ccb5de3df9ac65e93dc5fd
Yanming Yang
admin_assigned
2025-09-19T10:40:32.159Z
66431f02f7c5de57fc858e20
/avatars/bc5f52b2f66cb4ee751d5d676c5b8777.svg
yanming Yang
user
2hiTee
68ccb5de3df9ac65e93dc5fe
Tong Zhao
68ccb5de3df9ac65e93dc5ff
Ruibo Li
68ccb5de3df9ac65e93dc600
Chi Zhang
admin_assigned
2025-09-19T10:40:46.830Z
62f058cb0acc344c17163141
/avatars/6368f20bd02a468b3443e7c65ffb9ec7.svg
Chi Zhang
user
ChiZhang
WorldForge: Unlocking Emergent 3D/4D Generation in Video Diffusion Model
  via Training-Free Guidance
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.
WorldForge, a training-free framework, enhances video diffusion models with precise motion control and photorealistic content generation through recursive refinement, flow-gated latent fusion, and dual-path self-corrective guidance.
video diffusion models spatial intelligence tasks latent world priors controllability geometric inconsistency retraining fine-tuning WorldForge inference-time framework Intra-Step Recursive Refinement denoising step Flow-Gated Latent Fusion optical flow similarity Dual-Path Self-Corrective Guidance trajectory injection trajectory drift photorealistic content generation trajectory consistency visual fidelity plug-and-play paradigm generative priors