Viggle JST-2

JST-2 is Viggle's physics-native world foundation model for generating 3D scenes, characters and full-body motion in one shared world. Built by Viggle, which describes itself as a spatial GenAI platform, the model is centered on characters: it takes one image, or text, and returns a complete full-body 3D character with motion dynamics already built in, ready to animate without separate rigging and skinning steps. The same model generates 3D scenes, which Viggle says take about one second, and produces motion from text or video. It can be tried free in the PINOC browser app, used through Viggle's API, or accessed via MCP with agents such as Claude Code. The character research page documents how the model was tested and where it still falls short. In a conventional animation or game pipeline, a generated character is only the starting point. Rigging and skinning are specialist work measured in hours, and once a rig is built it decides how the character moves for the rest of its life. JST-2 targets exactly that handoff: instead of building a shape and then attaching a rig, it builds the character and its motion dynamics in one generation, so a single input image or prompt yields something already animation-ready. A second problem is input quality. Real creative inputs are not studio plates, and lighting, angle, framing and occlusion are all over the place. Characters are often cropped, turned away, or missing hands and feet, and the model is designed to complete those partial inputs into a body whose structure and proportions hold together. JST-2 generates 3D characters from a single image or from text. In Viggle's blind human tests against Meshy 7.1 and Tripo H3.1, the model led both baselines on character generation, with its strongest results on facial detail. Face quality scored 63.0% against Meshy 7.1 and 82.0% against Tripo H3.1, where a score of 50% means equal preference and draws count as half a win. Facial detail is what preserves a character's identity in 3D, which matters when the asset is your own digital double, an actor's stand-in, or an IP character you will reuse for years. Texture quality, body quality and clothing quality were also rated above both baselines: against Meshy 7.1 the model averaged 58.4% overall, and against Tripo H3.1 it averaged 73.2%. The model also completes characters from cropped or partially occluded images across a wide range of visual styles. When only the upper body is in frame, when the subject is turned away, or when hands and feet are hidden, JST-2 extends the visible region into a full body whose structure and proportions hold together. Viggle notes that on clean images all three tested models are usable and the gap opens on difficult ones, and that real creative work rarely starts from a clean image. The test set reflects this: alongside the photoreal, stylized, cartoon, anime and sci-fi categories used by major commercial 3D asset platforms, it adds hard samples drawn from what users actually upload, including unusual poses, occluded subjects, off-axis viewpoints, bodies cropped out of frame, difficult lighting, low-resolution and blurred images, and busy backgrounds. Motion is where the model is most differentiated. On animation readiness, defined as how a generated character moves once it is driven, JST-2 led Meshy 7.1 with an average score of 73.8% and Tripo H3.1 with 88.9%. The criteria were joint alignment, rig usability, deformation quality and hand rigging. Hand rigging reached 82.8% against Meshy 7.1 and 89.3% against Tripo H3.1, while deformation quality reached 77.6% and 93.2% respectively. Every finger moves on its own, and once the character moves, clothing, hair and accessories deform with the body rather than fighting it. All of this happens in a single generation: complete body, 3D character and motion dynamics at once, with no remesh and no separate rigging step left for the user. Under the hood, JST stands for Joint Space-and-Time. The model represents the world as persistent atoms in 3D space: each atom represents a piece of matter or an object part and retains its identity as it moves and deforms, carrying an explicit xyz position plus learned vectors for its attributes and dynamics. These atoms are encoded as Physics-Native Tokens, and the JST decoder turns them into 3D Gaussian splats. A JST-2 character is built from these tokens, and the dynamics vectors inside its Physics-Native Tokens encode how each part deforms under different motions. Because those dynamics are learned from data rather than authored by hand, the character arrives ready to animate without separate rigging and skinning steps. The practical benefit is removing a specialist bottleneck. Rigging and skinning normally take hours of expert work, and the resulting rig locks in how the character can move for its whole life. With JST-2 the character and its motion dynamics come out of one generation, so teams can go from a single image or prompt to something they can drive. Identity is preserved in 3D through stronger facial detail, which helps when the same character is reused across projects. Because the model handles cropped, occluded and awkwardly framed inputs, teams can work from material they already have rather than requiring studio-grade photography. And since scenes, characters and motion are generated in one shared world, the outputs are meant to fit together rather than being assembled from separate tools. Use cases follow directly from the benchmarks. Viggle names digital doubles, actor stand-ins and IP characters that will be reused for years as the kinds of assets where preserved facial detail matters. Game and animation pipelines benefit from characters that arrive without a rigging step, with hands that articulate finger by finger and clothing that deforms with the body. Teams working from user-uploaded or imperfect imagery can complete partially framed subjects into full bodies. Beyond characters, the model generates 3D scenes in about one second and produces motion from text or video, and it can be driven through an API or through MCP with agents like Claude Code, which supports automated and agent-based workflows. The product is aimed at people building 3D characters and scenes: character artists, animators, game and animation production teams, and developers who want to call the model programmatically. Access options named in the content are the PINOC browser app, where it can be tried free, the Viggle API with a developer portal and documentation, and MCP for use with agents such as Claude Code. Viggle also lists related products including Meme Maker, PINOC, Games and the API. The technical foundation described is the Joint Space-and-Time representation, Physics-Native Tokens and 3D Gaussian splats, with learned dynamics driving character motion. Viggle is explicit about current limits. Gaussian artefacts can appear around limb joints, and in some cases the shoulder-to-torso join, the armpit and the gaps between fingers show holes, merging or collapse, which the page calls the most visible failure mode. Surface detail has a ceiling: hair, clothing and materials read cleanly at normal viewing distance but soften as the camera moves in. Unusual bodies that depart from normal human proportions, such as very long limbs, an exaggerated head-to-body ratio or a non-humanoid silhouette, can produce off joint placement and deformation. Clothing, hair and accessories deform with the body but are not simulated, so they do not swing from inertia or collide with the body or each other; scene collisions and object interactions still rely on rule-based physics. Viggle says upcoming releases will improve how complex clothing deforms during motion and how hair moves, and that JST-3 is in development to replace those rules with learned dynamics. In summary, JST-2's value proposition is a single model that produces complete, animation-ready 3D characters from one image or text, with motion dynamics built in rather than rigged afterwards, backed by Viggle's own blind human benchmarks against Meshy 7.1 and Tripo H3.1 and extended by scene generation and text- or video-driven motion in one shared world.