Score Jacobian Chaining (SJC) によるテキストからの3D生成を試す｜npaka
Score Jacobian Chaining. Lifting pretrained 2d diffusion models for 3d generation. We propose to apply chain rule on the learned.
Web the key insight is to interpret diffusion models as learned predictors of a gradient field, often referred to as the score. Web ∇ θ log q σ (θ) ⏟ 3d score = e π [ ∇ x π log p σ (x π) ⏟ 2d score; We propose to apply chain rule on the learned. A diffusion model learns to. Lifting pretrained 2d diffusion models for 3d generation. A diffusion model learns to predict a vector field of gradients. Pretrained ⋅ j π ∇ x π log p (x π) ⏟ renderer jacobian].
A diffusion model learns to predict a vector field of gradients. Lifting pretrained 2d diffusion models for 3d generation. A diffusion model learns to predict a vector field of gradients. Pretrained ⋅ j π ∇ x π log p (x π) ⏟ renderer jacobian]. We propose to apply chain rule on the learned. Web ∇ θ log q σ (θ) ⏟ 3d score = e π [ ∇ x π log p σ (x π) ⏟ 2d score; Web the key insight is to interpret diffusion models as learned predictors of a gradient field, often referred to as the score. A diffusion model learns to.
