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Off The Grid: Detection of Primitives for Feed-Forward 3D Gaussian Splatting Contributions: 1. A novel solution to detect primitives across images, achieving higher performance and improved efficiency than pixel-aligned approaches. 2. A multi-density adaptive mechanism, enabling dynamic allocation of more or fewer primitives depending on image patch content. 3. A more effective method to fine-tune a 3D reconstruction model with rendering supervision, yielding improvements in camera pose estimation. 4. A pose-free feed-forward Gaussian Splatting model that outperforms existing methods on novel view synthesis and camera pose estimation.
Paper: https://t.co/YW8aTNbkQQ
I've seen people running 20+ blogs on Outrank (not 1 or 2 customers, there are a LOT of them) insane how this feature is valuable
Built Tweet Hunter, Taplio (sold $8m) Growing https://t.co/OyNJ8ZUyOh - https://t.co/jS9GQJ5Ps8 - https://t.co/EFUcKeBbpU - https://t.co/JkVOl1O0S1 - https://t.co/KG9PgxJabg Sharing weekly tips about growth: https://t.co/ereQodN3Ov
藏师傅字节 Seedance 1.5 Pro 视频模型首测！ 这次支持了音画同出，更惊喜的是还支持方言生成，同时表演中的情绪表达和复杂运镜也都有非常大的进步 可以去火山引擎、豆包和即梦尝试，先看一下藏师傅的测试视频混剪 下面是具体的测试和提示词👇
懒得等施工🚧可以直接在这里看长文： https://t.co/L6UdEtrxlC
What's your go-to approach for structuring large Laravel projects? I can't recommend THIS Reddit answer enough. Full post: https://t.co/hHnLSmB3uH
~20 yrs in web-dev, now mostly Laravel. My Laravel courses: https://t.co/HRUAJdMRZL My Youtube channel: https://t.co/qPQAkaov2F
Gaussian Pixel Codec Avatars: A Hybrid Representation for Efficient Rendering Abstract (excerpt): GPiCA utilizes a unique hybrid representation combining a triangle mesh and anisotropic 3D Gaussians. This combination maximizes memory and rendering efficiency while maintaining a photorealistic appearance. - The triangle mesh is highly efficient in representing surface areas like facial skin. - The 3D Gaussians effectively handle non-surface areas such as hair and beard. To this end, we develop a unified differentiable rendering pipeline that treats the mesh as a semi-transparent layer within the volumetric rendering paradigm of 3D Gaussian Splatting. We train neural networks to decode a facial expression code into three components: 1. A 3D face mesh, 2. An RGBA texture, 3. A set of 3D Gaussians. These components are rendered simultaneously in a unified rendering engine. The networks are trained using multi-view image supervision. Our results demonstrate that GPiCA achieves the realism of purely Gaussian-based avatars while matching the rendering performance of mesh-based avatars.
Paper: https://t.co/O8FNPwXCeR
RT @kevinma_dev_zh: 尤哥推荐的这本书非常好，推荐大家买来学习。 我再推荐一本我们全家都非常喜欢的书：《I Can Do That!: 1000 Ways to Become Independent》 中文可译为《我能做到！：1000个变得独立的方法》，是…
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