Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training | Paper Citation Record

Observation 1967d700-2bbe-4b63-8ba7-a9429ef72384 · outbound

This paper cites A morphable model for the synthesis of 3d faces.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training A morphable model for the synthesis of 3d faces

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Observation b4666724-8896-4f0a-84b7-3e5b1d87ef22 · outbound

This paper cites Partgen: Part-level 3d generation and reconstruction with multi-view diffusion models.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Partgen: Part-level 3d generation and reconstruction with multi-view diffusion models

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Observation 2ebfb712-e433-4d06-afab-1fa3cc10e1e0 · outbound

This paper cites AutoPartGen: Autogressive 3D Part Generation and Discovery.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training AutoPartGen: Autogressive 3D Part Generation and Discovery

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Observation 837f551e-9b10-4aa6-a781-efc2a1f756af · outbound

This paper cites Ultra3D: Efficient and High-Fidelity 3D Generation with Part Attention.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Ultra3D: Efficient and High-Fidelity 3D Generation with Part Attention

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Observation be8b79b2-148f-4dc0-9e53-109d165a5132 · outbound

This paper cites MeshCoder: LLM-Powered Structured Mesh Code Generation from Point Clouds.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training MeshCoder: LLM-Powered Structured Mesh Code Generation from Point Clouds

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Observation 05b1086f-d4d1-4b17-9c84-74f1ab0f30ff · outbound

This paper cites Objaverse-xl: A universe of 10m+ 3d objects.Advances in Neural Informa- tion Processing Systems, 36:35799–35813, 2023.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Objaverse-xl: A universe of 10m+ 3d objects.Advances in Neural Informa- tion Processing Systems, 36:35799–35813, 2023

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This paper cites Objaverse: A universe of annotated 3d objects.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Objaverse: A universe of annotated 3d objects

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Observation 806b00f7-419f-4987-9afd-76996bfaaa15 · outbound

This paper cites From one to more: Contex- tual part latents for 3d generation.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training From one to more: Contex- tual part latents for 3d generation

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Observation 0a7d6c07-9f6a-4d7e-aae0-8edf376e872b · outbound

This paper cites Ip-composer: Semantic composition of visual concepts.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Ip-composer: Semantic composition of visual concepts

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Observation 73f6d013-da17-44b3-b606-ebca06e4f2d6 · outbound

This paper cites Distribution-Conditional Generation: From Class Distribution to Creative Generation.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Distribution-Conditional Generation: From Class Distribution to Creative Generation

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Observation 424b6bbc-47b8-47e2-8569-342631fcb412 · outbound

This paper cites Redefining¡ creative¿ in dictionary: Towards an enhanced se- mantic understanding of creative generation.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Redefining¡ creative¿ in dictionary: Towards an enhanced se- mantic understanding of creative generation

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Observation 7cc35508-f47e-423a-b73e-8375797a01c5 · outbound

This paper cites Seed3d 1.0: From images to high-fidelity simulation- ready 3d assets.arXiv preprint arXiv:2510.19944, 2025.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Seed3d 1.0: From images to high-fidelity simulation- ready 3d assets.arXiv preprint arXiv:2510.19944, 2025

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Observation 561d3412-2d0b-4a28-9849-583fc32b2efb · outbound

This paper cites Multi-view stereo: A tutorial.Foundations and trends® in Computer Graphics and Vision, 9(1-2):1–148, 2015.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Multi-view stereo: A tutorial.Foundations and trends® in Computer Graphics and Vision, 9(1-2):1–148, 2015

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Observation bf8313c6-59f5-425c-98d8-23cfc0665c6c · outbound

This paper cites An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

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Observation 5e778eb6-ac5c-4a99-a129-e6cc42015674 · outbound

This paper cites Tokenverse: Versatile multi-concept personalization in token modulation space.ACM Transactions On Graphics (TOG), 44(4):1–11, 2025.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Tokenverse: Versatile multi-concept personalization in token modulation space.ACM Transactions On Graphics (TOG), 44(4):1–11, 2025

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Observation f266b05f-d612-40b6-946a-78c9e796cc65 · outbound

This paper cites Mv-adapter: Multi-view consistent image generation made easy.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Mv-adapter: Multi-view consistent image generation made easy

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Observation c7e76f7e-5284-4e4f-9633-713f9f8f653f · outbound

This paper cites Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material

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Observation 36314e6b-8154-4429-8974-53a00f3c17ab · outbound

This paper cites 3d gaussian splatting for real-time radiance field rendering.ACM Trans.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training 3d gaussian splatting for real-time radiance field rendering.ACM Trans

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Observation 13d6ce38-b2ea-4f8e-8d62-fe9d9dfc7bb0 · outbound

This paper cites FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space

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Observation e62afc8c-775c-4634-8212-8a52c56fa8bc · outbound

This paper cites Hunyuan3D 2.5: Towards High-Fidelity 3D Assets Generation with Ultimate Details.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Hunyuan3D 2.5: Towards High-Fidelity 3D Assets Generation with Ultimate Details

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Observation 410fa0d9-a5e1-4c4a-8bf9-7232a8c3223b · outbound

This paper cites Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Model.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Model

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Observation 5806d9a3-d6b1-46b6-bd00-69b66e7efbf3 · outbound

This paper cites Dreambeast: Distilling 3d fantastical animals with part-aware knowledge transfer.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Dreambeast: Distilling 3d fantastical animals with part-aware knowledge transfer

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Observation 2fb815dd-2efe-43d5-8825-3e0f1215e277 · outbound

This paper cites CraftsMan3D: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refiner.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training CraftsMan3D: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refiner

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Observation d7495f2a-718a-4a29-bd62-0bdc233c4c8b · outbound

This paper cites Connecting consistency distillation to score distillation for text-to-3d generation.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Connecting consistency distillation to score distillation for text-to-3d generation

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Observation 3aec1879-8cb8-4199-be07-962dfe8c4cbd · outbound

This paper cites Sparc3D: Sparse Representation and Construction for High-Resolution 3D Shapes Modeling.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Sparc3D: Sparse Representation and Construction for High-Resolution 3D Shapes Modeling

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This paper cites Luciddreamer: Towards high- fidelity text-to-3d generation via interval score matching.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Luciddreamer: Towards high- fidelity text-to-3d generation via interval score matching

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Observation cd884d6d-91a9-4795-b899-44bce090841e · outbound

This paper cites PartCrafter: Structured 3D Mesh Generation via Compositional Latent Diffusion Transformers.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training PartCrafter: Structured 3D Mesh Generation via Compositional Latent Diffusion Transformers

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Observation bb673ed3-1d4a-4464-83b2-ac5673b411d8 · outbound

This paper cites Evaluating text-to-visual generation with image-to-text gen- eration.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Evaluating text-to-visual generation with image-to-text gen- eration

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This paper cites Dreamreward-x: Boosting high-quality 3d generation with human preference alignment.IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, 2025.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Dreamreward-x: Boosting high-quality 3d generation with human preference alignment.IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, 2025

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Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training SyncDreamer: Generating Multiview-consistent Images from a Single-view Image

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Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Wonder3d: Sin- gle image to 3d using cross-domain diffusion

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Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Shading meets motion: Self-supervised indoor 3d reconstruction via simultaneous shape-from-shading and structure-from-motion

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Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Data synthesis with diverse styles for face recognition via 3dmm-guided diffusion

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This paper cites Nerf: Representing scenes as neural radiance fields for view syn- thesis.Communications of the ACM, 65(1):99–106, 2021.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Nerf: Representing scenes as neural radiance fields for view syn- thesis.Communications of the ACM, 65(1):99–106, 2021

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Observation d44a748e-6d35-4591-9c5f-319009c0caa3 · outbound

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Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Partcraft: Crafting creative objects by parts

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Observation 622ad34b-8688-449d-b94e-6a766bf59e4c · outbound

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Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Object-level Visual Prompts for Compositional Image Generation

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Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Nested attention: Semantic-aware attention values for concept personalization

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Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training DreamFusion: Text-to-3D using 2D Diffusion

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Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Apply hierarchical- chain-of-generation to complex attributes text-to-3d gener- ation

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Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Learning transferable visual models from natural language supervi- sion

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Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training pops: Photo-inspired diffusion operators

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Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Piece it Together: Part-Based Concepting with IP-Priors

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Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training High-resolution image synthesis with latent diffusion models

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Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

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Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training In- stantbooth: Personalized text-to-image generation without test-time finetuning

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Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training MVDream: Multi-view Diffusion for 3D Generation

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This paper cites Chimera: 10 Compositional image generation using part-based concept- ing.arXiv preprint arXiv:2510.18083, 2025.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Chimera: 10 Compositional image generation using part-based concept- ing.arXiv preprint arXiv:2510.18083, 2025

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Observation 3b803a55-57f2-45e7-9372-887ac7b5a08b · outbound

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Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Puppeteer: Rig and Animate Your 3D Models

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Observation fb3d1c43-f8d8-4554-b6b0-bfad67b342bb · outbound

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Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Dreamgaussian: Generative gaussian splatting for effi- cient 3d content creation

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Observation 1fbab379-69c9-4fe9-a1ec-125f722066ba · outbound

This paper cites Efficient Part-level 3D Object Generation via Dual Volume Packing.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Efficient Part-level 3D Object Generation via Dual Volume Packing

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Observation 19f8754f-9e00-41ec-a22e-9a1e88e13cd4 · outbound

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Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation

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Observation 3ede459a-b62f-4ce9-a222-e0f2a4bb3bf2 · outbound

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Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction

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Observation 15af38fa-4243-4923-b2a1-c0645f8d89be · outbound

This paper cites Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distilla- tion.Advances in neural information processing systems, 36: 8406–8441, 2023.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distilla- tion.Advances in neural information processing systems, 36: 8406–8441, 2023

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Observation ac0a4714-026c-4ac5-8a07-3c7dd18e8a49 · outbound

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Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Ouroboros3d: Image-to-3d generation via 3d- aware recursive diffusion

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Observation 1385eb40-12a9-45a5-84c9-944c6e4ca407 · outbound

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Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Direct3d: Scal- able image-to-3d generation via 3d latent diffusion trans- former.Advances in Neural Information Processing Systems, 37:121859–121881, 2024

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Observation 1a151d56-3f35-429f-8e46-63e055cc6fa7 · outbound

This paper cites Less-to-More Generalization: Unlocking More Controllability by In-Context Generation.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Less-to-More Generalization: Unlocking More Controllability by In-Context Generation

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Observation 6c4ddc14-e384-42a6-9620-fcf239302607 · outbound

This paper cites Direct3D-S2: Gigascale 3D Generation Made Easy with Spatial Sparse Attention.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Direct3D-S2: Gigascale 3D Generation Made Easy with Spatial Sparse Attention

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Observation 5dadd6d0-c84b-4aea-8501-5ce0b489f07d · outbound

This paper cites Structured 3d latents for scalable and versatile 3d gen- eration.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Structured 3d latents for scalable and versatile 3d gen- eration

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Observation 2cfa69c0-2903-4335-8f23-03dc91de06f0 · outbound

This paper cites Frankenstein: Generating semantic- compositional 3d scenes in one tri-plane.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Frankenstein: Generating semantic- compositional 3d scenes in one tri-plane

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Observation bb83ee5f-0401-4667-9492-7ea75fe0554c · outbound

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Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training PhyCAGE: Physically Plausible Compositional 3D Asset Generation from a Single Image

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Observation ce69adbd-0de4-43ce-9419-f0654d472467 · outbound

This paper cites X-part: high fidelity and structure coherent shape decomposition, 2025.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training X-part: high fidelity and structure coherent shape decomposition, 2025

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Source-reported events for the cited work

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Observation 25c781c8-4df1-4116-b32e-86736c609570 · outbound

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Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training X-part: high fidelity and structure coher- ent shape decomposition.arXiv preprint arXiv:2509.08643,

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Observation 12e32558-fe19-41ec-bc9a-026a40579932 · outbound

This paper cites Qwen3 Technical Report.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Qwen3 Technical Report

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Source-reported events for the cited work

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Observation 0060cf91-f19f-4b81-8c6c-c833c7556626 · outbound

This paper cites HoloPart: Generative 3D Part Amodal Segmentation.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training HoloPart: Generative 3D Part Amodal Segmentation

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Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Wonder3d++: Cross-domain diffusion for high-fidelity 3d generation from a single im- age.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025

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Observation 9f5d6a49-9f73-4c5d-8661-0b1674ed709b · outbound

This paper cites OmniPart: Part-Aware 3D Generation with Semantic Decoupling and Structural Cohesion.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training OmniPart: Part-Aware 3D Generation with Semantic Decoupling and Structural Cohesion

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Observation f0f2c4f7-45dc-4239-834e-85faefdbf61c · outbound

This paper cites Mvsnet: Depth inference for unstructured multi-view stereo.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Mvsnet: Depth inference for unstructured multi-view stereo

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Observation 2205f01a-d98a-4eb3-b506-ba2a87a42b2a · outbound

This paper cites Hi3DGen: High-fidelity 3D Geometry Generation from Images via Normal Bridging.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Hi3DGen: High-fidelity 3D Geometry Generation from Images via Normal Bridging

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Observation 0f0c249a-08ed-427e-8ae7-9846404cbad8 · outbound

This paper cites IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models

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Observation 0d5e7449-4bd5-4827-8984-f85d39ba1aae · outbound

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Observation 00c0f5a9-6284-48c2-810e-ec39406e2e55 · outbound

This paper cites Clay: A controllable large-scale generative model for creat- ing high-quality 3d assets.ACM Transactions on Graphics (TOG), 43(4):1–20, 2024.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Clay: A controllable large-scale generative model for creat- ing high-quality 3d assets.ACM Transactions on Graphics (TOG), 43(4):1–20, 2024

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Observation c73ffb6e-b0fa-4707-b22c-b9d0a02d992a · outbound

This paper cites Bang: Dividing 3d assets via generative exploded dynamics.ACM Transactions on Graphics (TOG), 44(4):1–21, 2025.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Bang: Dividing 3d assets via generative exploded dynamics.ACM Transactions on Graphics (TOG), 44(4):1–21, 2025

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Observation ba652964-5555-4896-b203-24665c871120 · outbound

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Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Shape-from-shading: a survey.IEEE trans- actions on pattern analysis and machine intelligence, 21(8): 690–706, 2002

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Observation 25fa8be4-11f7-4c19-8f1d-aaad35910c1a · outbound

This paper cites Michelangelo: Conditional 3d shape generation based on shape-image-text aligned latent representation.Advances in neural information processing systems, 36:73969–73982,.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Michelangelo: Conditional 3d shape generation based on shape-image-text aligned latent representation.Advances in neural information processing systems, 36:73969–73982,

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Source-reported events for the cited work

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Observation 3fec647e-e43c-44f3-b746-0774caae0163 · outbound

This paper cites Segmentdreamer: Towards high-fidelity text- to-3d synthesis with segmented consistency trajectory distil- lation.

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training Segmentdreamer: Towards high-fidelity text- to-3d synthesis with segmented consistency trajectory distil- lation

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