Paper Citation Record · LEDGER

Motion Generation: A Survey of Generative Approaches and Benchmarks

As of 22 August 2026, this Paper Citation Record lists 100 of 193 outbound references and 5 inbound Pith citation observations for arXiv:2507.05419.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.05419 v1

Coverage vector

measured 100 of 193 reference resolution

Typed states for the displayed outbound observations.

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measured 105 of 105 standing notices

One-hop event checks from named stored sources.

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measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T17:30:19.672312Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T16:39:57.259001Z

Reference resolution

100 of 193 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 87cf6269-5f73-4544-8f1a-2548deb8ca93 · outbound

This paper cites Unpaired motion style transfer from video to animation.

Motion Generation: A Survey of Generative Approaches and Benchmarks Unpaired motion style transfer from video to animation

Reference 1

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Observation 43012510-9758-4433-a555-024a02986196 · outbound

This paper cites Skel-betweener: a neural motion rig for interactive motion authoring.

Motion Generation: A Survey of Generative Approaches and Benchmarks Skel-betweener: a neural motion rig for interactive motion authoring

Reference 2

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Observation 2d251bfd-1132-4de2-a551-a4322ded95a2 · outbound

This paper cites Text2action: Generative adversarial synthesis from language to action.

Motion Generation: A Survey of Generative Approaches and Benchmarks Text2action: Generative adversarial synthesis from language to action

Reference 3

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Observation 43e0cc8e-3a90-4ffd-a6d4-c9a6297020a8 · outbound

This paper cites Lan- guage2pose: Natural language grounded pose forecast- ing.

Motion Generation: A Survey of Generative Approaches and Benchmarks Lan- guage2pose: Natural language grounded pose forecast- ing

Reference 4

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Observation ab6e0882-c7bc-4a4e-828c-d762e9b6d173 · outbound

This paper cites Hmd-nemo: Online 3d avatar motion generation from sparse obser- vations.

Motion Generation: A Survey of Generative Approaches and Benchmarks Hmd-nemo: Online 3d avatar motion generation from sparse obser- vations

Reference 5

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Observation 0c68b4df-5dd8-4240-800f-f42a887d5cd7 · outbound

This paper cites Lead: Latent realignment for human motion diffusion.

Motion Generation: A Survey of Generative Approaches and Benchmarks Lead: Latent realignment for human motion diffusion

Reference 6

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Source-reported events for the cited work

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Observation 2a55bd2c-165a-49a6-9445-5e1948285120 · outbound

This paper cites Circle: Capture in rich contextual environments.

Motion Generation: A Survey of Generative Approaches and Benchmarks Circle: Capture in rich contextual environments

Reference 7

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Source-reported events for the cited work

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Observation 6252a45e-d07a-4d89-ab69-4812c3f409b0 · outbound

This paper cites Emotion control of unstruc- tured dance movements.

Motion Generation: A Survey of Generative Approaches and Benchmarks Emotion control of unstruc- tured dance movements

Reference 8

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Observation 4cd7a0a6-fb0d-4b7d-b761-afbb0b10147d · outbound

This paper cites Motionfix: Text-driven 3d human motion editing.

Motion Generation: A Survey of Generative Approaches and Benchmarks Motionfix: Text-driven 3d human motion editing

Reference 9

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Observation e06fbcd5-fc9d-4a23-affd-e7b83bfa6115 · outbound

This paper cites Teach: Temporal action composition for 3d humans.

Motion Generation: A Survey of Generative Approaches and Benchmarks Teach: Temporal action composition for 3d humans

Reference 10

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Observation 3cc0e922-dd2a-410e-940f-52cb9ff65e45 · outbound

This paper cites Make-an-animation: Large- scale text-conditional 3d human motion generation.

Motion Generation: A Survey of Generative Approaches and Benchmarks Make-an-animation: Large- scale text-conditional 3d human motion generation

Reference 11

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Observation 95caa401-c9e4-4c46-bb7c-d6296dcc15c5 · outbound

This paper cites Versatile physics-based character control with hybrid latent representation.

Motion Generation: A Survey of Generative Approaches and Benchmarks Versatile physics-based character control with hybrid latent representation

Reference 12

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Observation c6c98301-24ef-4192-bba1-406c8db108fb · outbound

This paper cites Belfusion: Latent diffusion for behavior- driven human motion prediction.

Motion Generation: A Survey of Generative Approaches and Benchmarks Belfusion: Latent diffusion for behavior- driven human motion prediction

Reference 13

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Observation 038a1af5-ef54-467c-993d-05f7c37b97bc · outbound

This paper cites Seamless human motion composition with blended positional encodings.

Motion Generation: A Survey of Generative Approaches and Benchmarks Seamless human motion composition with blended positional encodings

Reference 14

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Observation 0e328d92-3a50-4e50-8156-68a16a779550 · outbound

This paper cites Bedlam: A synthetic dataset of bodies ex- hibiting detailed lifelike animated motion.

Motion Generation: A Survey of Generative Approaches and Benchmarks Bedlam: A synthetic dataset of bodies ex- hibiting detailed lifelike animated motion

Reference 15

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Observation 3f1d0458-a54e-4263-b254-417109127989 · outbound

This paper cites Humman: Multi-modal 4d human dataset for versatile sensing and modeling.

Motion Generation: A Survey of Generative Approaches and Benchmarks Humman: Multi-modal 4d human dataset for versatile sensing and modeling

Reference 16

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Observation 4f198517-b039-4cdc-aeb3-51b275458aca · outbound

This paper cites Gen- erating human motion in 3d scenes from text descrip- tions.

Motion Generation: A Survey of Generative Approaches and Benchmarks Gen- erating human motion in 3d scenes from text descrip- tions

Reference 17

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Observation 93da222b-97bc-40f9-8de7-819c4d909202 · outbound

This paper cites Implicit neural representations for variable length human motion generation.

Motion Generation: A Survey of Generative Approaches and Benchmarks Implicit neural representations for variable length human motion generation

Reference 18

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Observation 169925e9-ac56-4f92-8a82-3c20c79a5bd3 · outbound

This paper cites Rethinking human motion prediction with symplectic integral.

Motion Generation: A Survey of Generative Approaches and Benchmarks Rethinking human motion prediction with symplectic integral

Reference 19

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Observation 1a10ed3a-8958-4adc-ad9b-5c2400c0dc74 · outbound

This paper cites Humanmac: Masked motion completion for human motion prediction.

Motion Generation: A Survey of Generative Approaches and Benchmarks Humanmac: Masked motion completion for human motion prediction

Reference 20

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Observation 2e3ded2d-075d-4565-a728-f3708104c643 · outbound

This paper cites Taming diffusion probabilis- tic models for character control.

Motion Generation: A Survey of Generative Approaches and Benchmarks Taming diffusion probabilis- tic models for character control

Reference 21

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Observation ef221a25-daea-4596-ac01-38331c16349e · outbound

This paper cites Executing your commands via motion diffusion in latent space.

Motion Generation: A Survey of Generative Approaches and Benchmarks Executing your commands via motion diffusion in latent space

Reference 22

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Observation a3fe5a07-dc27-4f56-b311-590f1be50c3f · outbound

This paper cites Cmu graphics lab motion capture database.

Motion Generation: A Survey of Generative Approaches and Benchmarks Cmu graphics lab motion capture database

Reference 23

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Observation e956a239-328b-4572-b55f-76219a36d080 · outbound

This paper cites Flexible motion in- betweening with diffusion models.

Motion Generation: A Survey of Generative Approaches and Benchmarks Flexible motion in- betweening with diffusion models

Reference 24

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Observation 7e59bf34-ec2a-425a-9c65-2d0735faaad6 · outbound

This paper cites Mofusion: A frame- work for denoising-diffusion-based motion synthesis.

Motion Generation: A Survey of Generative Approaches and Benchmarks Mofusion: A frame- work for denoising-diffusion-based motion synthesis

Reference 25

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Observation 2e2555b7-5aac-4133-8ed6-301fec06caba · outbound

This paper cites Motionlcm: Real-time controllable motion generation via latent consistency model.

Motion Generation: A Survey of Generative Approaches and Benchmarks Motionlcm: Real-time controllable motion generation via latent consistency model

Reference 26

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Observation 3698a795-658c-4e01-97ae-922a02a68f1a · outbound

This paper cites Generative adver- sarial graph convolutional networks for human action synthesis.

Motion Generation: A Survey of Generative Approaches and Benchmarks Generative adver- sarial graph convolutional networks for human action synthesis

Reference 27

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Observation a826ff51-3fef-4fb6-89b5-f8aadd08089c · outbound

This paper cites Pos- escript: 3d human poses from natural language.

Motion Generation: A Survey of Generative Approaches and Benchmarks Pos- escript: 3d human poses from natural language

Reference 28

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Observation 9afd8ee1-6f40-47d5-8341-c598fe90bc2b · outbound

This paper cites Diffusion models beat gans on image synthesis.

Motion Generation: A Survey of Generative Approaches and Benchmarks Diffusion models beat gans on image synthesis

Reference 29

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Observation 500007bf-c87e-4a2c-a55d-59f6ab6c8d77 · outbound

This paper cites Wandr: Intention-guided human motion generation.

Motion Generation: A Survey of Generative Approaches and Benchmarks Wandr: Intention-guided human motion generation

Reference 30

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Observation ee1a48b6-3c18-48ae-afa5-f46068ac9c6a · outbound

This paper cites Investigating the use of recurrent motion modelling for speech gesture generation.

Motion Generation: A Survey of Generative Approaches and Benchmarks Investigating the use of recurrent motion modelling for speech gesture generation

Reference 31

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Observation 36f07d16-df24-4a3d-8be7-265e9a57b464 · outbound

This paper cites Recurrent network models for human dynamics.

Motion Generation: A Survey of Generative Approaches and Benchmarks Recurrent network models for human dynamics

Reference 32

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Observation 1777fab2-2645-4207-89c9-30f00cf6b332 · outbound

This paper cites Decompose more and aggregate better: Two closer looks at frequency representation learning for human motion prediction.

Motion Generation: A Survey of Generative Approaches and Benchmarks Decompose more and aggregate better: Two closer looks at frequency representation learning for human motion prediction

Reference 33

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Observation d3e7e75f-313c-4aad-b285-9d3c622a1a57 · outbound

This paper cites Anytop: Character animation diffusion with any topology.

Motion Generation: A Survey of Generative Approaches and Benchmarks Anytop: Character animation diffusion with any topology

Reference 34

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Observation 6297b8f3-a697-4121-ae10-ff217d4fe989 · outbound

This paper cites Synthesis of compositional animations from textual descriptions.

Motion Generation: A Survey of Generative Approaches and Benchmarks Synthesis of compositional animations from textual descriptions

Reference 35

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This paper cites Imos: Intent- driven full-body motion synthesis for human-object interactions.

Motion Generation: A Survey of Generative Approaches and Benchmarks Imos: Intent- driven full-body motion synthesis for human-object interactions

Reference 36

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This paper cites Generative adversarial nets.

Motion Generation: A Survey of Generative Approaches and Benchmarks Generative adversarial nets

Reference 37

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This paper cites Momask: Generative masked modeling of 3d human motions.

Motion Generation: A Survey of Generative Approaches and Benchmarks Momask: Generative masked modeling of 3d human motions

Reference 38

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This paper cites Generative Human Motion Stylization in Latent Space.

Motion Generation: A Survey of Generative Approaches and Benchmarks Generative Human Motion Stylization in Latent Space

Reference 39

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This paper cites Generating diverse and natural 3d human motions from text.

Motion Generation: A Survey of Generative Approaches and Benchmarks Generating diverse and natural 3d human motions from text

Reference 40

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Observation 242831ec-822e-4801-a674-cc77438942c3 · outbound

This paper cites Tm2t: Stochastic and tokenized modeling for the re- ciprocal generation of 3d human motions and texts.

Motion Generation: A Survey of Generative Approaches and Benchmarks Tm2t: Stochastic and tokenized modeling for the re- ciprocal generation of 3d human motions and texts

Reference 41

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This paper cites Action2motion: Conditioned generation of 3d human motions.

Motion Generation: A Survey of Generative Approaches and Benchmarks Action2motion: Conditioned generation of 3d human motions

Reference 42

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This paper cites Back to mlp: A simple baseline for human motion prediction.

Motion Generation: A Survey of Generative Approaches and Benchmarks Back to mlp: A simple baseline for human motion prediction

Reference 43

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This paper cites MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm.

Motion Generation: A Survey of Generative Approaches and Benchmarks MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm

Reference 44

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This paper cites Robust motion in-betweening.

Motion Generation: A Survey of Generative Approaches and Benchmarks Robust motion in-betweening

Reference 45

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This paper cites Stochastic scene-aware motion prediction.

Motion Generation: A Survey of Generative Approaches and Benchmarks Stochastic scene-aware motion prediction

Reference 46

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This paper cites Resolving 3d human pose ambiguities with 3d scene constraints.

Motion Generation: A Survey of Generative Approaches and Benchmarks Resolving 3d human pose ambiguities with 3d scene constraints

Reference 47

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This paper cites Hinton and Ruslan R.

Motion Generation: A Survey of Generative Approaches and Benchmarks Hinton and Ruslan R

Reference 48

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This paper cites Denoising diffusion probabilistic models.

Motion Generation: A Survey of Generative Approaches and Benchmarks Denoising diffusion probabilistic models

Reference 49

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This paper cites Como: Control- lable motion generation through language guided pose code editing.

Motion Generation: A Survey of Generative Approaches and Benchmarks Como: Control- lable motion generation through language guided pose code editing

Reference 50

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This paper cites Adobe mixamo.

Motion Generation: A Survey of Generative Approaches and Benchmarks Adobe mixamo

Reference 51

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Motion Generation: A Survey of Generative Approaches and Benchmarks Unresolved cited work

Reference 52

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This paper cites HGM³: Hierarchical genera- tive masked motion modeling with hard token mining.

Motion Generation: A Survey of Generative Approaches and Benchmarks HGM³: Hierarchical genera- tive masked motion modeling with hard token mining

Reference 53

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This paper cites A large-scale rgb-d database for arbitrary-view human action recognition.

Motion Generation: A Survey of Generative Approaches and Benchmarks A large-scale rgb-d database for arbitrary-view human action recognition

Reference 54

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This paper cites Motiongpt: Human motion as a foreign language.

Motion Generation: A Survey of Generative Approaches and Benchmarks Motiongpt: Human motion as a foreign language

Reference 55

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This paper cites Motionchain: Conver- sational motion controllers via multimodal prompts.

Motion Generation: A Survey of Generative Approaches and Benchmarks Motionchain: Conver- sational motion controllers via multimodal prompts

Reference 56

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This paper cites Local action-guided motion diffusion model for text-to-motion generation.

Motion Generation: A Survey of Generative Approaches and Benchmarks Local action-guided motion diffusion model for text-to-motion generation

Reference 57

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This paper cites Act as you wish: Fine-grained control of motion diffusion model with hierarchical semantic graphs.

Motion Generation: A Survey of Generative Approaches and Benchmarks Act as you wish: Fine-grained control of motion diffusion model with hierarchical semantic graphs

Reference 58

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This paper cites Dafnet: Generating diverse actions for furniture interaction by learning conditional pose distribution.

Motion Generation: A Survey of Generative Approaches and Benchmarks Dafnet: Generating diverse actions for furniture interaction by learning conditional pose distribution

Reference 59

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Observation 922bfcd4-44b3-4715-b16f-fc7ab0e9e586 · outbound

This paper cites Physics- informed machine learning.

Motion Generation: A Survey of Generative Approaches and Benchmarks Physics- informed machine learning

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This paper cites Guided motion dif- fusion for controllable human motion synthesis.

Motion Generation: A Survey of Generative Approaches and Benchmarks Guided motion dif- fusion for controllable human motion synthesis

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This paper cites UniMoGen: Universal Motion Generation.

Motion Generation: A Survey of Generative Approaches and Benchmarks UniMoGen: Universal Motion Generation

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This paper cites Flame: Free-form language-based motion synthesis & editing.

Motion Generation: A Survey of Generative Approaches and Benchmarks Flame: Free-form language-based motion synthesis & editing

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Motion Generation: A Survey of Generative Approaches and Benchmarks Body gesture generation for multimodal conversational agents

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This paper cites Auto-Encoding Variational Bayes.

Motion Generation: A Survey of Generative Approaches and Benchmarks Auto-Encoding Variational Bayes

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This paper cites Priority- centric human motion generation in discrete latent space.

Motion Generation: A Survey of Generative Approaches and Benchmarks Priority- centric human motion generation in discrete latent space

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This paper cites Moving fast and slow: Analysis of representations and post-processing in speech-driven automatic gesture generation.

Motion Generation: A Survey of Generative Approaches and Benchmarks Moving fast and slow: Analysis of representations and post-processing in speech-driven automatic gesture generation

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This paper cites Nifty: Neural object interaction fields for guided human motion synthesis.

Motion Generation: A Survey of Generative Approaches and Benchmarks Nifty: Neural object interaction fields for guided human motion synthesis

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Motion Generation: A Survey of Generative Approaches and Benchmarks Multiact: Long-term 3d human motion generation from multiple action labels

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This paper cites Convolutional sequence to sequence model for human dynamics.

Motion Generation: A Survey of Generative Approaches and Benchmarks Convolutional sequence to sequence model for human dynamics

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Motion Generation: A Survey of Generative Approaches and Benchmarks Foot-constrained spatial- temporal transformer for keyframe-based complex mo- tion synthesis

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This paper cites Finedance: A fine-grained choreography dataset for 3d full body dance generation.

Motion Generation: A Survey of Generative Approaches and Benchmarks Finedance: A fine-grained choreography dataset for 3d full body dance generation

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Motion Generation: A Survey of Generative Approaches and Benchmarks Ai choreographer: Music conditioned 3d dance generation with aist++

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This paper cites Aamdm: accelerated auto- regressive motion diffusion model.

Motion Generation: A Survey of Generative Approaches and Benchmarks Aamdm: accelerated auto- regressive motion diffusion model

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Motion Generation: A Survey of Generative Approaches and Benchmarks Controlling character motions without observ- able driving source

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This paper cites MulSMo: Multimodal Stylized Motion Generation by Bidirectional Control Flow.

Motion Generation: A Survey of Generative Approaches and Benchmarks MulSMo: Multimodal Stylized Motion Generation by Bidirectional Control Flow

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This paper cites LaMP: Language-Motion Pretraining for Motion Generation, Retrieval, and Captioning.

Motion Generation: A Survey of Generative Approaches and Benchmarks LaMP: Language-Motion Pretraining for Motion Generation, Retrieval, and Captioning

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This paper cites Omg: Towards open-vocabulary motion generation via mixture of controllers.

Motion Generation: A Survey of Generative Approaches and Benchmarks Omg: Towards open-vocabulary motion generation via mixture of controllers

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This paper cites Hybridcap: Inertia-aid monocular capture of challenging human motions.

Motion Generation: A Survey of Generative Approaches and Benchmarks Hybridcap: Inertia-aid monocular capture of challenging human motions

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This paper cites Intergen: Diffusion-based multi-human motion generation under complex interactions.

Motion Generation: A Survey of Generative Approaches and Benchmarks Intergen: Diffusion-based multi-human motion generation under complex interactions

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This paper cites Motion- x: A large-scale 3d expressive whole-body human mo- tion dataset.

Motion Generation: A Survey of Generative Approaches and Benchmarks Motion- x: A large-scale 3d expressive whole-body human mo- tion dataset

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This paper cites Being comes from not-being: Open-vocabulary text-to-motion generation with wordless training.

Motion Generation: A Survey of Generative Approaches and Benchmarks Being comes from not-being: Open-vocabulary text-to-motion generation with wordless training

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This paper cites VersatileMotion: A Unified Framework for Motion Synthesis and Comprehension.

Motion Generation: A Survey of Generative Approaches and Benchmarks VersatileMotion: A Unified Framework for Motion Synthesis and Comprehension

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Motion Generation: A Survey of Generative Approaches and Benchmarks Unresolved cited work

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This paper cites Beat: A large-scale semantic and emotional multi-modal dataset for conversational gestures synthesis.

Motion Generation: A Survey of Generative Approaches and Benchmarks Beat: A large-scale semantic and emotional multi-modal dataset for conversational gestures synthesis

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Observation 1f1a5285-1f9a-499f-84b2-c5c4a4a7f0b9 · outbound

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Motion Generation: A Survey of Generative Approaches and Benchmarks Programmable motion generation for open-set motion control tasks

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This paper cites Plan, posture and go: Towards open-vocabulary text-to-motion generation.

Motion Generation: A Survey of Generative Approaches and Benchmarks Plan, posture and go: Towards open-vocabulary text-to-motion generation

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This paper cites Ntu rgb+ d 120: A large-scale benchmark for 3d human activity under- standing.

Motion Generation: A Survey of Generative Approaches and Benchmarks Ntu rgb+ d 120: A large-scale benchmark for 3d human activity under- standing

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This paper cites Learning hierarchical cross-modal association for co-speech gesture generation.

Motion Generation: A Survey of Generative Approaches and Benchmarks Learning hierarchical cross-modal association for co-speech gesture generation

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This paper cites Smpl: a skinned multi-person linear model.

Motion Generation: A Survey of Generative Approaches and Benchmarks Smpl: a skinned multi-person linear model

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This paper cites Posegpt: Quantization-based 3d human motion generation and forecasting.

Motion Generation: A Survey of Generative Approaches and Benchmarks Posegpt: Quantization-based 3d human motion generation and forecasting

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This paper cites M$^3$GPT: An Advanced Multimodal, Multitask Framework for Motion Comprehension and Generation.

Motion Generation: A Survey of Generative Approaches and Benchmarks M$^3$GPT: An Advanced Multimodal, Multitask Framework for Motion Comprehension and Generation

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This paper cites 3d human motion prediction: A survey.

Motion Generation: A Survey of Generative Approaches and Benchmarks 3d human motion prediction: A survey

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Observation 236f81af-ec6d-4f9d-b648-04f28aec1dd7 · outbound

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Motion Generation: A Survey of Generative Approaches and Benchmarks Amass: Archive of motion capture as surface shapes

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Observation 57106664-014e-4b8e-8c0c-74d70ac508a1 · outbound

This paper cites Generation of complex 3d human motion by temporal and spatial composition of diffusion models.

Motion Generation: A Survey of Generative Approaches and Benchmarks Generation of complex 3d human motion by temporal and spatial composition of diffusion models

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Motion Generation: A Survey of Generative Approaches and Benchmarks Human motion prediction using recurrent neural net- works

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Motion Generation: A Survey of Generative Approaches and Benchmarks Real- time style modelling of human locomotion via feature- wise transformations and local motion phases

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This paper cites Monocular 3d human pose estimation in the wild using improved cnn supervision.

Motion Generation: A Survey of Generative Approaches and Benchmarks Monocular 3d human pose estimation in the wild using improved cnn supervision

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Motion Generation: A Survey of Generative Approaches and Benchmarks Absolute Coordinates Make Motion Generation Easy

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Motion Generation: A Survey of Generative Approaches and Benchmarks Rethinking Diffusion for Text-Driven Human Motion Generation: Redundant Representations, Evaluation, and Masked Autoregression

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Pith citing papers

Observation 6a930fb2-672b-4cbe-baf6-c27ce517d4ec · inbound

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FunPhase: A Periodic Functional Autoencoder for Motion Generation via Phase Manifolds Motion Generation: A Survey of Generative Approaches and Benchmarks

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IRG-MotionLLM: Interleaving Motion Generation, Assessment and Refinement for Text-to-Motion Generation Motion Generation: A Survey of Generative Approaches and Benchmarks

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Coordinate-Based Dual-Constrained Autoregressive Motion Generation Motion Generation: A Survey of Generative Approaches and Benchmarks

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Social Structure Matters in 3D Human-Human Interaction Generation Motion Generation: A Survey of Generative Approaches and Benchmarks

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Observation e7eb131d-f5f3-4249-ad6b-adb59bff84c4 · inbound

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Towards Continual Motion-Language Agents: LoRA Variants for Incremental Motion Understanding and Generation Motion Generation: A Survey of Generative Approaches and Benchmarks

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