We show that SDS is a special case of VSD and leads to poor samples with both small and large CFG weights. In comparison, VSD works well with various CFG weights as ancestral sampling from diffusion models and simultaneously improves the diversity and sample quality with a common CFG weight (i.e.,

Table 5: All task variations except shape used in VLMbench. Table 6: All object models used in VLMbench. Object type Number of classes Classes Basic model 3 cube (1), triangular prism (1), cylinder (1)Special model 9 star (1), moon (1), cross (1), flower (1), letter't' (1), pencil (1), basket (1), box container(1), shape sorter (1)Planar model 6 rectangle (1), circle (1), triangle (1), star (1), cross (1), flower (1)Functional model 2 mug (6), sponge (1) Articulated model 2 door with one rotatble handle (2), cabinet with three vertical drawers (3) In the VLMbench, we show eight task categories:"Pick & Place objects", "Stack objects", "Drop When building an instance-level task with one variation, the other variations will also randomly change. For example, in the demonstrations of "Pick & Place objects" In the dataset, we have five types of objects, shown in Table 6. Visualizations can be found on the project website. The object can be placed anywhere with any orientation inside the container. When the detector is triggered, the task considers a success. Instruction T emplates: High-level instructions: "Pick up [target object description] and place it into [target container description]."; Low-level instructions: ("Move to the top of [target object "Move the object into [target container description]; V ariations and scene settings: All objects are randomly changing colors, size, and positions in each demonstration. Color: There are two same-shape objects and two same-shape containers in the scene initialization. All colors are randomly sampled from the color library. The object description is "[color] object"; The container description is "[color] container." Size: There are two same-shape objects and two same-shape containers in the scene initialization. One object and one container are randomly magnified while others are randomly shrunk. Relative Position: There are two same-shape objects and two same-shape containers in the scene initialization. The object description is "[front/rear/left/right] object"; The container description The number of objects varies from two to the length of the object library. High-level instructions: "Stack [below object description] and [above object Low-level instructions: ("Move to the top of [above object description]; "Move the object on [below object description]; Release the Object models: In the seen settings, five object models: star, triangular, cylinder, cube, moon.

Score distillation sampling (SDS) has shown great promise in text-to-3D generation by distilling pretrained large-scale text-to-image diffusion models, but suffers from over-saturation, over-smoothing, and low-diversity problems. In this work, we propose to model the 3D parameter as a random variable instead of a constant as in SDS and present variational score distillation (VSD), a principled particle-based variational framework to explain and address the aforementioned issues in text-to-3D generation. We show that SDS is a special case of VSD and leads to poor samples with both small and large CFG weights. In comparison, VSD works well with various CFG weights as ancestral sampling from diffusion models and simultaneously improves the diversity and sample quality with a common CFG weight (i.e., $7.5$). We further present various improvements in the design space for text-to-3D such as distillation time schedule and density initialization, which are orthogonal to the distillation algorithm yet not well explored. Our overall approach, dubbed ProlificDreamer, can generate high rendering resolution (i.e., $512\times512$) and high-fidelity NeRF with rich structure and complex effects (e.g., smoke and drops). Further, initialized from NeRF, meshes fine-tuned by VSD are meticulously detailed and photo-realistic. Project page and codes: https://ml.cs.tsinghua.edu.cn/prolificdreamer/

Benefiting from language flexibility and compositionality, humans naturally intend to use language to command an embodied agent for complex tasks such as navigation and object manipulation. In this work, we aim to fill the blank of the last mile of embodied agents -- object manipulation by following human guidance, e.g., "move the red mug next to the box while keeping it upright." To this end, we introduce an Automatic Manipulation Solver (AMSolver) system and build a Vision-and-Language Manipulation benchmark (VLMbench) based on it, containing various language instructions on categorized robotic manipulation tasks. Specifically, modular rule-based task templates are created to automatically generate robot demonstrations with language instructions, consisting of diverse object shapes and appearances, action types, and motion constraints. We also develop a keypoint-based model 6D-CLIPort to deal with multi-view observations and language input and output a sequence of 6 degrees of freedom (DoF) actions. We hope the new simulator and benchmark will facilitate future research on language-guided robotic manipulation.