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Interesting take on world model consistency: what if you skip 3D reconstruction entirely? WorldCrafter from TencentARC, weights are open. Instead of building 3D geometry, the video generator queries an implicit memory by camera pose. point the camera somewhere and the memory fills in the view. → one image or...

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Tencent AI

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11,229 просмотров • 3 дней назад •via X (Twitter)

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Фото профиля Lily Anderson
Lily Anderson3 дней назад

WorldCrafter is a fascinating approach to world-model consistency. I’d love to collaborate with Tencent AI and help amplify your latest AI innovations through my creator network. Open to discussing promotional opportunities!

Фото профиля LatentRail
LatentRail2 дней назад

How does the memory behave when the camera revisits a place from a very different path?

Фото профиля RAZA | AI EXPLORER
RAZA | AI EXPLORER2 дней назад

Skipping 3D reconstruction is a fascinating direction. WorldCrafter looks seriously promising.

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18,903 просмотров • 10 месяцев назад

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China open-sourced a model that reconstructs any scene in 3D from a regular video, in real-time. one camera. no LiDAR. 10,000+ frames without falling apart. just walk around with your camera and watch the entire world get rebuilt in 3D at 20 fps. → runs at ~20 FPS on a single GPU → Stable over 10,000+ frames → Beats optimization-based methods on benchmarks → Works on drone footage, driving videos, indoor walkthroughs 100% open source.

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1,457,247 просмотров • 2 месяцев назад

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62,768 просмотров • 3 лет назад

🔥Excited to introduce Tencent Hunyuan3D-Omni, the industry's first 3D asset creation system with multi-condition control. Built on the open-source Hunyuan3D 2.1 as the "ControlNet of 3D," it addresses single-view occlusions, and poor geometry by integrating up to four control conditions. Two core innovations power the system: a Lightweight Unified Control Encoder for efficient multimodal fusion, and a Progressive Difficulty-Aware Training Strategy that boosts robustness. ✅Under single-image conditions, skeletal input provides precise pose control for animation and avatars. ✅Point cloud input—full or depth-projected—removes visual ambiguity and yields accurate photorealistic geometry. ✅Bounding box control adjusts L/W/H ratios to match design scale. ✅Voxel control sculpts topology for engineering and creative workflows. We are open-sourcing inference code and weights to accelerate controllable 3D research and industry deployment. Code: Weights: Tech Report: #3DGenAI #TencentHunyuan #OpenSourceAI
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🔥Excited to introduce Tencent Hunyuan3D-Omni, the industry's first 3D asset creation system with multi-condition control. Built on the open-source Hunyuan3D 2.1 as the "ControlNet of 3D," it addresses single-view occlusions, and poor geometry by integrating up to four control conditions. Two core innovations power the system: a Lightweight Unified Control Encoder for efficient multimodal fusion, and a Progressive Difficulty-Aware Training Strategy that boosts robustness. ✅Under single-image conditions, skeletal input provides precise pose control for animation and avatars. ✅Point cloud input—full or depth-projected—removes visual ambiguity and yields accurate photorealistic geometry. ✅Bounding box control adjusts L/W/H ratios to match design scale. ✅Voxel control sculpts topology for engineering and creative workflows. We are open-sourcing inference code and weights to accelerate controllable 3D research and industry deployment. Code: Weights: Tech Report: #3DGenAI #TencentHunyuan #OpenSourceAI

Tencent Hy

34,898 просмотров • 1 год назад

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34,651 просмотров • 1 месяц назад

Meet LA-Pose. Our latest model taking Wayve another step towards generalization at scale. LA-Pose employs large-scale self-supervised learning, building strong motion representations for 3D perception from 10.2 million unlabeled driving video snippets, unlike today's strongest approaches that often depend on expensive, carefully curated 3D supervision. With only a lightweight pose head and limited labelled data, LA-Pose achieves: 📷 State-of-the-art camera pose estimation 🌎 Strong zero-shot generalization across diverse driving scenarios 🏷️ Orders of magnitude less labelled data than fully supervised 3D approaches Our full blog post: Explore the full paper here:
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Meet LA-Pose. Our latest model taking Wayve another step towards generalization at scale. LA-Pose employs large-scale self-supervised learning, building strong motion representations for 3D perception from 10.2 million unlabeled driving video snippets, unlike today's strongest approaches that often depend on expensive, carefully curated 3D supervision. With only a lightweight pose head and limited labelled data, LA-Pose achieves: 📷 State-of-the-art camera pose estimation 🌎 Strong zero-shot generalization across diverse driving scenarios 🏷️ Orders of magnitude less labelled data than fully supervised 3D approaches Our full blog post: Explore the full paper here:

Wayve

36,960 просмотров • 4 месяцев назад

Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors paper page: present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture. In both stages, the 3D content is learned through reference view supervision and novel views guided by a combination of 2D and 3D diffusion priors. We introduce a single trade-off parameter between the 2D and 3D priors to control exploration (more imaginative) and exploitation (more precise) of the generated geometry. Additionally, we employ textual inversion and monocular depth regularization to encourage consistent appearances across views and to prevent degenerate solutions, respectively. Magic123 demonstrates a significant improvement over previous image-to-3D techniques, as validated through extensive experiments on synthetic benchmarks and diverse real-world images.
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Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors paper page: present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture. In both stages, the 3D content is learned through reference view supervision and novel views guided by a combination of 2D and 3D diffusion priors. We introduce a single trade-off parameter between the 2D and 3D priors to control exploration (more imaginative) and exploitation (more precise) of the generated geometry. Additionally, we employ textual inversion and monocular depth regularization to encourage consistent appearances across views and to prevent degenerate solutions, respectively. Magic123 demonstrates a significant improvement over previous image-to-3D techniques, as validated through extensive experiments on synthetic benchmarks and diverse real-world images.

AK

305,729 просмотров • 3 лет назад

We are introducing Hunyuan3D-Part: an open-source part-level 3D shape generation model that outperforms all existing open and close-source models. Highlights: 🔹P3-SAM: The industry's first native 3D part segmentation model. 🔹X-Part: A part generation model that achieves state-of-the-art results in controllability and shape quality. Key-features: 1️⃣Eliminates the use of 2D SAM during training, relying solely on a large-scale dataset with 3.7 million shapes and clean part annotations. 2️⃣Introduces a new automated segmentation pipeline in 3D without user intervention. 3️⃣Implements a diffusion-based part decomposition pipeline utilizing both geometry and semantic clues. Code: Weights: Tech reports: 🔸P3-SAM： → Paper: → Project page: 🔸X-Part： → Paper: → Project page: Try it now： → (Light version) Hugging Face demo: → (Full version) Hunyuan3D Studio:
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We are introducing Hunyuan3D-Part: an open-source part-level 3D shape generation model that outperforms all existing open and close-source models. Highlights: 🔹P3-SAM: The industry's first native 3D part segmentation model. 🔹X-Part: A part generation model that achieves state-of-the-art results in controllability and shape quality. Key-features: 1️⃣Eliminates the use of 2D SAM during training, relying solely on a large-scale dataset with 3.7 million shapes and clean part annotations. 2️⃣Introduces a new automated segmentation pipeline in 3D without user intervention. 3️⃣Implements a diffusion-based part decomposition pipeline utilizing both geometry and semantic clues. Code: Weights: Tech reports: 🔸P3-SAM： → Paper: → Project page: 🔸X-Part： → Paper: → Project page: Try it now： → (Light version) Hugging Face demo: → (Full version) Hunyuan3D Studio:

Tencent Hy

72,640 просмотров • 1 год назад