DeepMotion vs Text-To-4D

DeepMotion builds 3D character animation from text prompts or reference video through a browser, with no mocap suit required. Its two products, SayMotion and Animate 3D, turn written motion descriptions or uploaded MP4, MOV, and AVI clips into exportable skeleton data for game and film pipelines. Output ships as FBX, BVH, GLB, or MP4 files you can retarget to custom characters.

Traditional motion capture needs specialized hardware and studio space. DeepMotion runs markerless full-body tracking in the cloud, so indie studios and solo animators can produce fight choreography or dance moves from a phone video. SayMotion adds generative inpainting to extend or blend motions beyond stock libraries, which stock animation sites cannot match without manual editing.

Game developers, XR artists, film students, and educators use it to prototype character motion before committing to a full rigging pass. The Free Animation Credit Program lets users earn back credits by labeling motion descriptions or correcting poses in the Rotoscope editor.

Text-To-4D is a Meta AI research project (MAV3D) that generates three-dimensional dynamic scenes from text descriptions. The method uses a 4D dynamic Neural Radiance Field optimized for appearance, density, and motion consistency by querying a text-to-video diffusion model.

Unlike static 3D generators that output a single mesh, Text-To-4D produces scenes you can view from any camera angle and composite into other 3D environments. The approach needs no 3D or 4D training data; the underlying text-to-video model trains only on text-image pairs and unlabeled videos.

The project page hosts demo samples for text-to-4D prompts like "a corgi playing with a ball" and image-to-4D conversions from still photos. It is a research showcase, not a commercial product with a public API or signup. Researchers and 3D artists interested in NeRF-based dynamic scene generation can explore the paper and sample outputs on the site.