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wmdrift

Diagnostics for autoregressive video world models - quantifies camera drift, temporal jitter, memory attenuation, and quality decay from rollouts.

Details

External ID
1367932755
Source
GITHUB
Company
—
Product
wmdrift
Website domain
github.com
Launched
Sept. 13, 2026
Cohort
—
Upvotes
16
Upvotes percentile
0.5194722008711248
Tags
clip, computer-vision, diagnostics, evaluation, lpips, opencv, pytorch, video-generation, visual-odometry, world-models
Fetched at
Sept. 17, 2026, 5:02 p.m.
Updated at
Sept. 17, 2026, 5:02 p.m.

Enrichment

Theme
ai video generation and editing tools
Vertical
Horizontal
Function
Observability & eval
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
diagnostic evaluation toolkit for video world models
Manually corrected
False

Could you build this?

Partial The CLI harness and reporting metrics can be vibe-coded, but calculating precise camera drift, temporal jitter, and rollout decay across autoregressive video world models requires specialized computer vision and optical flow evaluation pipelines.

What it would actually take: Requires integrating computer vision toolkits (RAFT/FlowNet for optical flow, Structure from Motion / COLMAP for camera pose estimation, and perceptual quality metrics like FVD/LPIPS). The primary difficulty is robustly separating camera motion drift from semantic object deformation across autoregressive rollouts without ground-truth 3D tracks. Requires domain knowledge in generative video architectures and 3D vision geometry.

Competitors

Other products that read as similar to this one — 765 launches clear the similarity bar, closest 8 shown.

Attention rank: #375 of 766 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 317 days after the earliest competitor.

Other launches for this product

Same idea, different domain

Nobody's really built a observability & eval tool for Media & entertainment yet.