3d-generation

Nicolas Zullo demonstrates a workflow using Codex and img2threejs to generate 3D game assets from text prompts, automatically integrating them into a game engine.

DiffGI introduces a differentiable geometry image representation for high-fidelity thin-shell 3D generation, enabling end-to-end optimization and superior reconstruction quality.

Hallo4D is a model-agnostic framework that leverages large multimodal language models to detect and correct spatial and temporal hallucinations in 3D and 4D generation, improving consistency across viewpoints and time without requiring retraining.

A new model enables generating 3D models from a single image locally on Apple Silicon devices and iPhones, using less than 2GB RAM and completing in under 20 seconds.

SynCity 3000 introduces a framework for generating large, globally coherent 3D scenes by adapting image-to-3D generators as convolutional operators, fine-tuned on synthetic scene data from a new data engine.

PixWorld presents a unified pixel-space diffusion approach for 3D scene reconstruction and generation, overcoming limitations of latent-space methods by using direct image-level supervision and geometry-aware feature alignment. The method outperforms prior generation methods and matches state-of-the-art reconstruction methods.

CONFLUX is a 3D latent diffusion model for chest CT synthesis that achieves high-fidelity volumetric generation with controllable clinical attributes, enhanced by a reinforcement learning post-training stage to improve conditioning reliability. The model and a synthetic dataset of ~200k chest CT volumes are released.

This article covers how to run Hunyuan3D, an AI model that converts images to 3D objects, directly on an iPhone.

FLAT proposes a method to decode explicit triangle splats directly from video diffusion latents for geometrically accurate 3D scene generation. It introduces a ray-centered rotation parameterization and a product window function to improve gradient flow, achieving better geometric accuracy than prior feedforward methods while supporting real-time rendering.

FLUX3D introduces a framework for high-fidelity image-to-3D Gaussian Splatting generation by enhancing representation learning and cross-modal alignment with diffusion-aligned structured latents and a sparse-structure-aware diffusion transformer, achieving state-of-the-art results.

Arbor introduces explicit geometric control for 3D asset generation by using constraint meshes (hull, avoidance, touch regions) to condition latent generation, improving spatial constraint adherence without sacrificing object quality.

JanusMesh is a fast, training-free framework that generates text-driven 3D visual illusions—a single mesh revealing different semantics from different viewing angles—by decoupling generation into cross-space dual-branch denoising and view-conditioned texture synthesis, achieving high realism in just 3-5 minutes.

This paper introduces TopVAE, a topology-optimized VAE that reduces 'dark areas' in molecular latent diffusion by making the decoder internalize structural and chemical constraints, achieving significant improvements in molecular generation quality.

Fable AI demonstrated the ability to generate an entire Hogwarts castle in one shot, including classrooms, the Great Hall, and Quidditch pitch.

World Labs announces three new research papers focused on generating 3D content using large-scale generative models and 2D priors, led by interns Hao Zhang, Bert Duisterhof, and Ben Tunnels.

This tweet discusses the implications of Claude Fable 5's one-shot generation of a 3D explorable world of a baby in the womb, raising concerns about deepfakes, dataset provenance, and synthetic medical content.

DiffusionGemma is out; it's compute-bound and 4x faster than other Gemma-4 models with 1k tok/s on H100, and excels at coding tasks including 3D generation and front-end.

This paper introduces P3D-Bench, a benchmark for evaluating multimodal large language models on parametric 3D generation tasks, including text-to-3D, image-to-3D, and assembly-3D, with metrics for geometric precision, semantic alignment, and part-level structure.

A new open-source tool called Nova3D uses an LLM to generate 3D objects with separate logical parts (e.g., a microwave with internal assembly and a swinging door), unlike monolithic AI generators. It writes Blender Python code to create clean, modular GLB exports suitable for animation and editing.

BrickAnything is an autoregressive framework that generates physically buildable brick structures from diverse 3D representations using point clouds and structure-aware tree tokenization, ensuring geometric fidelity and structural stability.