OmniShowcase makes 360-degree videos of an object from four photos

OmniShowcase is a video generation method from FLIPTION and KAIST AI that makes a video of a specific object from four photos of it, including shots that orbit all the way around. The paper, posted on arXiv on October 26, 2026, was the second most upvoted entry on Hugging Face's daily papers page on October 29. It reports clearly better identity and 3D consistency than four baselines, at a small cost in image aesthetics.

The problem: a camera that sees what the photos never showed

Say you want a product video of a sneaker. You have a few photos of it. A video model has to invent every side of the shoe the photos don't show, and when the camera swings around the back, it often invents a different shoe. Researchers call this task subject-to-video generation.

The authors name two causes. Most methods take one reference image, so the unseen sides are pure guesswork. The ones that take several views don't give the model any explicit 3D structure to connect them. And the training data rarely guarantees that two viewpoints show the same 3D object.

What the team did

They fixed the data first. A script renders training videos in Blender from 3D models, using 81-frame camera orbits, with four reference views taken 90 degrees apart. The rendered object is then placed into a generated scene using a video inpainting model, and a vision-language model filters out bad clips. The result is Showcase-4K: 4,000 videos from 2,593 assets in 620 categories, drawn from Objaverse, the Digital Twin Catalog and Google Scanned Objects. The authors put the curation cost at about $120, roughly $25 per thousand clips kept.

Then they added geometry. From the four reference photos, they reconstruct a rough 3D model of the object, and render it as a 25-frame turntable of surface-normal maps. A normal map is an image that colors each pixel by the direction the surface faces, so it shows shape without color or texture. The video model gets this turntable as extra context. It doesn't have to line up with the output frames, so the model reads it as a description of the shape rather than a template to copy.

The video model underneath is Wan2.1 I2V-14B-480P, kept frozen. The team trained a LoRA adapter (a small add-on layer, rank 128) for 33,000 steps on four NVIDIA B200 GPUs, about 50 hours.

Results

The test sets are ShowcaseBench, which the authors built from 50 subjects that share no 3D asset with the training data, and 35 subjects from the NAVI dataset. The baselines are Phantom and MAGREF (single-image models the authors fed four views), MV-S2V and 3DreamBooth.

On ShowcaseBench, OmniShowcase scores 0.799 on DINO identity similarity, against 0.707 for the best baseline, 3DreamBooth. On MEt3R, a 3D consistency measure where lower is better, it scores 0.190 against 0.325. On a ground-truth 3D distance metric, it scores 0.0824 against 0.1717. It leads on the same metrics on NAVI.

Ablations point to the geometry as the main driver. Dropping the normal-map turntable lowers DINO to 0.752 and raises the 3D distance to 0.2092. Using a textured render instead of normals, or thinning the turntable to 9 frames, also does worse. More photos help too: with one reference, DINO is 0.702, and with four it is 0.799.

In a user study with 44 people and 24 videos, OmniShowcase got 70.9% to 79.4% of the votes on each criterion. No baseline got more than 12.7%. These are the authors' own evaluations, with metrics and benchmark they designed.

Limits

• Its aesthetic score is the lowest of the five methods in both tables, for example 0.574 against 0.614 for MAGREF-MV on ShowcaseBench.
• Backgrounds can look synthetic, because only the subject is physically rendered. The scene is inpainted.
• Objects that change shape between photos, such as a subject in different poses, are not handled. The reconstruction merges the poses and the output settles on an averaged pose or tangles at transitions.
• In the appendix, single-view versions of Phantom and MAGREF show lower ground-truth 3D distance than their four-view versions in the main table. The paper doesn't explain this.
• Training depends on Gemini 2.5 Flash to filter data, so the pipeline is not fully open.

Why it matters

Product videos, game assets and e-commerce are the obvious uses: a few photos in, a turntable or fly-around out. The finding is also useful beyond this paper. A rough 3D reconstruction, fed to a video model as plain context, improved consistency more than the video model's own design did. That is a cheap pattern others can copy.

The authors say they will release Showcase-4K, ShowcaseBench and the data-generation code. The paper doesn't say whether the trained LoRA weights will be released, and no repository was linked at the time of writing. Details are on the project page.