Nothing Special   »   [go: up one dir, main page]

Lu et al., 2022 - Google Patents

The DeepMotion entry to the GENEA Challenge 2022

Lu et al., 2022

View PDF
Document ID
14702704399061690414
Author
Lu S
Feng A
Publication year
Publication venue
Proceedings of the 2022 International Conference on Multimodal Interaction

External Links

Snippet

This paper describes the method and evaluation results of our DeepMotion entry to the GENEA Challenge 2022. One difficulty in data-driven gesture synthesis is that there may be multiple viable gesture motions for the same speech utterance. Therefore the deterministic …
Continue reading at dl.acm.org (PDF) (other versions)

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
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • 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
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T9/00—Image coding, e.g. from bit-mapped to non bit-mapped
    • G06T9/001—Model-based coding, e.g. wire frame
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation

Similar Documents

Publication Publication Date Title
Zhang et al. Generating human motion from textual descriptions with discrete representations
Petrovich et al. TEMOS: Generating diverse human motions from textual descriptions
Harvey et al. Robust motion in-betweening
Li et al. Ganimator: Neural motion synthesis from a single sequence
Lau et al. Modeling spatial and temporal variation in motion data
Lu et al. The DeepMotion entry to the GENEA Challenge 2022
Lu et al. Humantomato: Text-aligned whole-body motion generation
Lu et al. Co-speech gesture synthesis using discrete gesture token learning
Foo et al. Ai-generated content (aigc) for various data modalities: A survey
US20230154089A1 (en) Synthesizing sequences of 3d geometries for movement-based performance
Ding et al. Enhance Image-to-Image Generation with LLaVA Prompt and Negative Prompt
Wang et al. OmniTokenizer: A Joint Image-Video Tokenizer for Visual Generation
Chandran et al. Facial Animation with Disentangled Identity and Motion using Transformers
Siyao et al. Bailando++: 3d dance gpt with choreographic memory
Ding et al. Enhance image-to-image generation with llava-generated prompts
Zhou et al. RoboDreamer: Learning Compositional World Models for Robot Imagination
Kim et al. Deep transformer based video inpainting using fast fourier tokenization
Voß et al. AQ-GT: a temporally aligned and quantized GRU-transformer for co-speech gesture synthesis
Zou et al. ParCo: Part-Coordinating Text-to-Motion Synthesis
Foo et al. Aigc for various data modalities: A survey
Chi et al. M2d2m: Multi-motion generation from text with discrete diffusion models
Chemburkar et al. Discrete Diffusion for Co-Speech Gesture Synthesis
Zhang et al. Dr2: Disentangled recurrent representation learning for data-efficient speech video synthesis
Abrol et al. Improving generative modelling in VAEs using multimodal prior
KR102310757B1 (en) Method for generating human motion using sequential networks and apparatus thereof