Paper Citation Record · LEDGER

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision

As of 10 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2608.01392.

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

pith.paper-citation-record.v1
2608.01392 v1

Coverage vector

measured 45 of 45 reference resolution

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measured 45 of 45 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

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45 of 45 outbound references displayed

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External citation measurements

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Outbound references

Observation 1d8eb615-bb3a-4df3-ba8c-b07aaedb640b · outbound

This paper cites Vivit: A video vision transformer.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Vivit: A video vision transformer

Reference 1

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Observation 1e3ab1af-bc8b-4f09-ae93-eefb8c2ba092 · outbound

This paper cites Seamless human motion composition with blended posi- tional encodings.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Seamless human motion composition with blended posi- tional encodings

Reference 2

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Observation 729720f7-9dcb-4e37-bb4f-45cc441c4bbc · outbound

This paper cites A cross- dataset study for text-based 3d human motion retrieval.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision A cross- dataset study for text-based 3d human motion retrieval

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:18:18.350693Z

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Observation 3f7840b3-31ac-44ba-a1e3-a313f6891a9d · outbound

This paper cites Is space-time attention all you need for video understanding? InIcml, page 4, 2021.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Is space-time attention all you need for video understanding? InIcml, page 4, 2021

Reference 4

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Observation 110b8fd9-c88c-4d7b-a86f-4d4abafa473c · outbound

This paper cites Sinkhorn distances: Lightspeed computation of optimal transport.Advances in neural information pro- cessing systems, 26, 2013.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Sinkhorn distances: Lightspeed computation of optimal transport.Advances in neural information pro- cessing systems, 26, 2013

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:18:18.086343Z

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Observation e1df4d46-a53f-4f95-8e51-83ea6a6c04b6 · outbound

This paper cites Segmo: Segment-aligned text to 3d human motion generation.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Segmo: Segment-aligned text to 3d human motion generation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:18:17.845673Z

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Observation 2d8e775e-2337-449d-bacd-e94edbb17081 · outbound

This paper cites Masked autoencoders as spatiotemporal learners.Advances in neural information processing systems, 35:35946–35958,.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Masked autoencoders as spatiotemporal learners.Advances in neural information processing systems, 35:35946–35958,

Reference 7

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Observation 9396af1c-2063-4a0b-af53-c00773747281 · outbound

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

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Generating diverse and natural 3d human motions from text

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:18:17.689155Z

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Observation a814d231-cf1b-4a36-a09b-b2ee7c0b31b5 · outbound

This paper cites Momask: Generative masked model- ing of 3d human motions.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Momask: Generative masked model- ing of 3d human motions

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:18:17.448771Z

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Observation 92145b61-a539-4f5f-bc8d-c4f3fe4662e8 · outbound

This paper cites Snapmogen: Human motion generation from expressive texts.arXiv preprint arXiv:2507.09122, 2025.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Snapmogen: Human motion generation from expressive texts.arXiv preprint arXiv:2507.09122, 2025

Reference 10

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Observation dd972f82-f165-447b-93fd-bb7817a4e9fc · outbound

This paper cites Amd: Autoregressive motion diffusion.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Amd: Autoregressive motion diffusion

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:18:17.223219Z

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Observation da7653cb-97cf-45b6-9b9b-a0f7481e01ed · outbound

This paper cites Como: Controllable motion generation through language guided pose code edit- ing.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Como: Controllable motion generation through language guided pose code edit- ing

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:18:17.019348Z

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Observation 4fbfc23a-42ea-4035-9321-2186955508ae · outbound

This paper cites Motiongpt: Human motion as a foreign lan- guage.Advances in Neural Information Processing Systems, 36:20067–20079, 2023.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Motiongpt: Human motion as a foreign lan- guage.Advances in Neural Information Processing Systems, 36:20067–20079, 2023

Reference 13

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Observation e9676649-5f43-4969-8a10-99ff725ef267 · outbound

This paper cites Unimotion: Unifying 3d human motion synthesis and understanding.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Unimotion: Unifying 3d human motion synthesis and understanding

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:18:16.656878Z

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Observation b6e88d32-1981-45cc-928b-82dd243fa9dd · outbound

This paper cites Frankenmotion: Part-level hu- man motion generation and composition.arXiv preprint arXiv:2601.10909, 2026.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Frankenmotion: Part-level hu- man motion generation and composition.arXiv preprint arXiv:2601.10909, 2026

Reference 15

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Observation 0baaaaff-ba9b-4d34-9537-72e342d8d4a2 · outbound

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

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Motion-x: A large-scale 3d expressive whole-body human motion dataset

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:18:16.419404Z

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Observation cabcb29f-76bb-468c-98c7-20dadfc5b32b · outbound

This paper cites Multi-granularity Correspondence Learning from Long-term Noisy Videos.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Multi-granularity Correspondence Learning from Long-term Noisy Videos

Reference 17

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Observation fc20a501-8fd9-45ba-8940-7ee9aff243e5 · outbound

This paper cites Rethinking diffusion for text-driven human motion generation: Redundant representations, evaluation, and masked autoregression.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Rethinking diffusion for text-driven human motion generation: Redundant representations, evaluation, and masked autoregression

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:18:16.238937Z

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Observation 424881ac-e4b6-4324-a00e-6e48c5cc6a0d · outbound

This paper cites Proposal-free temporal action detection via global segmen- tation mask learning.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Proposal-free temporal action detection via global segmen- tation mask learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:18:16.000195Z

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Observation ecee7a8d-abd9-4d16-b5e1-d65c7a3ea633 · outbound

This paper cites Tmr: Text-to-motion retrieval using contrastive 3d human motion synthesis.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Tmr: Text-to-motion retrieval using contrastive 3d human motion synthesis

Reference 20

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Observation 7fbdb2c5-6e48-471e-97f2-46202e197982 · outbound

This paper cites Now Foun- dations and Trends, 2019.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Now Foun- dations and Trends, 2019

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:18:15.475588Z

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Observation 9a584c9d-c0dd-48e8-a5ec-be2e508a0d10 · outbound

This paper cites Mmm: Generative masked motion model.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Mmm: Generative masked motion model

Reference 22

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Observation b977c382-ccef-4a8b-8949-22bc2cc3fab5 · outbound

This paper cites The kit motion-language dataset.Big data, 4(4):236–252,.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision The kit motion-language dataset.Big data, 4(4):236–252,

Reference 23

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This paper cites Babel: Bodies, action and behavior with english la- bels.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Babel: Bodies, action and behavior with english la- bels

Reference 24

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Observation 89c6b694-6741-4344-9dcb-dafa476b1c7c · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Learning transferable visual models from natural language supervi- sion

Reference 25

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FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Unresolved cited work

Reference 26

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Observation 232ce928-4530-4ff9-bb09-b7e511c4dfed · outbound

This paper cites Ot-clip: Un- derstanding and generalizing clip via optimal transport.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Ot-clip: Un- derstanding and generalizing clip via optimal transport

Reference 27

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Observation 6b0e7d07-a54a-420e-9339-8cf55db0588d · outbound

This paper cites OpenAI GPT-5 System Card.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision OpenAI GPT-5 System Card

Reference 28

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This paper cites Optimal transport on discrete domains.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Optimal transport on discrete domains

Reference 29

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Observation c6481434-0f9e-4afa-9cc2-cdf53114e0fb · outbound

This paper cites CoMA: Compositional Human Motion Generation with Multi-modal Agents.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision CoMA: Compositional Human Motion Generation with Multi-modal Agents

Reference 30

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Observation 3ca679e8-3bff-47bc-a24a-7f0a4d873767 · outbound

This paper cites Motionclip: Exposing human motion generation to clip space.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Motionclip: Exposing human motion generation to clip space

Reference 31

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Observation fcaeb8bd-9c66-4074-ae86-5bc2543f89f5 · outbound

This paper cites Human Motion Diffusion Model.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Human Motion Diffusion Model

Reference 32

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This paper cites Scaling Large Motion Models with Million-Level Human Motions.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Scaling Large Motion Models with Million-Level Human Motions

Reference 33

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Observation f40afea6-d756-4ab6-bf7a-4babacb90ddc · outbound

This paper cites Mg-motionllm: A unified framework for motion comprehension and gener- ation across multiple granularities.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Mg-motionllm: A unified framework for motion comprehension and gener- ation across multiple granularities

Reference 34

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Observation 65b39cf5-8667-4a57-afbd-b6db884a8c0c · outbound

This paper cites Dense motion captioning.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Dense motion captioning

Reference 35

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Observation f8392932-71c2-4c4c-acea-841098fe600c · outbound

This paper cites FILIP: Fine-grained Interactive Language-Image Pre-Training.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision FILIP: Fine-grained Interactive Language-Image Pre-Training

Reference 36

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Observation c779391c-f27a-4d81-80ab-4fbcf11f13e8 · outbound

This paper cites Generating human motion from textual descrip- tions with discrete representations.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Generating human motion from textual descrip- tions with discrete representations

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:18:13.891286Z

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Observation f3756519-1f9f-4f69-b1dc-2a1634116f49 · outbound

This paper cites Re- modiffuse: Retrieval-augmented motion diffusion model.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Re- modiffuse: Retrieval-augmented motion diffusion model

Reference 38

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This paper cites Finemogen: Fine-grained spatio- temporal motion generation and editing.Advances in Neural Information Processing Systems, 36:13981–13992, 2023.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Finemogen: Fine-grained spatio- temporal motion generation and editing.Advances in Neural Information Processing Systems, 36:13981–13992, 2023

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:18:13.726895Z

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Observation 8c455f42-98d8-4d38-b96b-1abb68d58607 · outbound

This paper cites Pre- training clip against data poisoning with optimal transport- based matching and alignment.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Pre- training clip against data poisoning with optimal transport- based matching and alignment

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:18:13.640874Z

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Observation 0973253c-3618-4620-86b4-6fcc9b32012d · outbound

This paper cites DartControl: A diffusion-based autoregressive motion model for real-time text-driven motion control.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision DartControl: A diffusion-based autoregressive motion model for real-time text-driven motion control

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:18:13.488133Z

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Observation 79f73070-d566-43e8-923e-878b1e21a7e2 · outbound

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FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Unresolved cited work

Reference 42

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Observation a6f6d641-ed17-44c8-8ab9-3f0f10634fdb · outbound

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FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision Unresolved cited work

Reference 43

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Observation 1845dedb-1e0b-4bd6-b0ef-58b78ec6c43d · outbound

This paper cites The annotation process involves man- ual segment-level boundary identification: for each action segmentj, human annotators specify the temporal interval [tstart, tend]in seconds.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision The annotation process involves man- ual segment-level boundary identification: for each action segmentj, human annotators specify the temporal interval [tstart, tend]in seconds

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:18:13.060514Z

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This paper cites specialized human motion analyst.

FineMoLA: Towards Fine-Grained Motion-Language Alignment from Clip-Level Supervision specialized human motion analyst

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:18:12.924197Z

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Pith citing papers

No inbound Pith citation observations are available.