Li et al., 2022 - Google Patents
Ganimator: Neural motion synthesis from a single sequenceLi et al., 2022
View PDF- Document ID
- 15509147056025524508
- Author
- Li P
- Aberman K
- Zhang Z
- Hanocka R
- Sorkine-Hornung O
- Publication year
- Publication venue
- ACM Transactions on Graphics (TOG)
External Links
Snippet
We present GANimator, a generative model that learns to synthesize novel motions from a single, short motion sequence. GANimator generates motions that resemble the core elements of the original motion, while simultaneously synthesizing novel and diverse …
- 230000002194 synthesizing 0 title abstract description 36
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T13/00—Animation
- G06T13/20—3D [Three Dimensional] animation
- G06T13/40—3D [Three Dimensional] animation of characters, e.g. humans, animals or virtual beings
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T15/00—3D [Three Dimensional] image rendering
- G06T15/10—Geometric effects
- G06T15/20—Perspective computation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2200/00—Indexing scheme for image data processing or generation, in general
- G06T2200/24—Indexing scheme for image data processing or generation, in general involving graphical user interfaces [GUIs]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2213/00—Indexing scheme for animation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2211/00—Image generation
- G06T2211/40—Computed tomography
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Li et al. | Ganimator: Neural motion synthesis from a single sequence | |
Po et al. | State of the art on diffusion models for visual computing | |
Starke et al. | Neural state machine for character-scene interactions | |
Mu et al. | A-sdf: Learning disentangled signed distance functions for articulated shape representation | |
Yuan et al. | Residual force control for agile human behavior imitation and extended motion synthesis | |
US10964084B2 (en) | Generating realistic animations for digital animation characters utilizing a generative adversarial network and a hip motion prediction network | |
Arikan et al. | Interactive motion generation from examples | |
Lewis et al. | Practice and theory of blendshape facial models. | |
Joshi et al. | Learning controls for blend shape based realistic facial animation | |
Grochow et al. | Style-based inverse kinematics | |
Lee et al. | Motion fields for interactive character locomotion | |
Min et al. | Interactive generation of human animation with deformable motion models | |
Lau et al. | Modeling spatial and temporal variation in motion data | |
He et al. | Nemf: Neural motion fields for kinematic animation | |
Ma et al. | Modeling style and variation in human motion | |
Casas et al. | 4D parametric motion graphs for interactive animation | |
Zhou et al. | Generative tweening: Long-term inbetweening of 3d human motions | |
Holden et al. | Learning an inverse rig mapping for character animation | |
Oreshkin et al. | Motion In-Betweening via Deep $\Delta $-Interpolator | |
Menapace et al. | Playable environments: Video manipulation in space and time | |
Lu et al. | The DeepMotion entry to the GENEA Challenge 2022 | |
Berseth et al. | Towards learning to imitate from a single video demonstration | |
Goel et al. | Interaction Mix and Match: Synthesizing Close Interaction using Conditional Hierarchical GAN with Multi‐Hot Class Embedding | |
Regateiro et al. | Deep4d: A compact generative representation for volumetric video | |
Zheng | One-to-many: Example-based mesh animation synthesis |