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4D-fy: Text-to-4D Generation Using Hybrid Score Distillation Sampling
Sherwin Bahmani, Ivan Skorokhodov, Victor Rong, Gordon Wetzstein, Leonidas Guibas, Peter Wonka, Sergey Tulyakov, Jeong Joon Park, Andrea Tagliasacchi, David B. Lindell
TL;DR
Text-to-4D generation must reconcile competing strengths of image, 3D-aware image, and video diffusion models across appearance, structure, and motion. 4D-fy introduces hybrid score distillation sampling, an alternating procedure that blends their supervision. The method produces high-quality dynamic 3D scenes, outperforming MAV3D in CLIP Score and user preference while achieving roughly comparable motion quality.
Problem
Text-to-4D methods face a three-way tradeoff among scene appearance, 3D structure, and motion when using different diffusion-model priors.
Method
Hybrid score distillation sampling alternates gradient updates from text-to-image, 3D-aware text-to-image, and text-to-video models across optimization stages.
Results
67% of users preferred 4D-fy over 33% for MAV3D overall, with significant preferences for appearance, 3D structure, text alignment, and overall quality, while motion was roughly on par.
Takeaways & Limitations
Hybrid SDS combines the strengths of multiple pretrained diffusion models to alleviate the appearance–3D-structure–motion tradeoff in text-to-4D generation.
Takeaways & Limitations
Temporal flickering remains a limitation, and emphasizing video supervision may reduce image quality.
Abstract
from arXiv · showhide
Recent breakthroughs in text-to-4D generation rely on pre-trained text-to-image and text-to-video models to generate dynamic 3D scenes. However, current text-to-4D methods face a three-way tradeoff between the quality of scene appearance, 3D structure, and motion. For example, text-to-image models and their 3D-aware variants are trained on internet-scale image datasets and can be used to produce scenes with realistic appearance and 3D structure -- but no motion. Text-to-video models are trained on relatively smaller video datasets and can produce scenes with motion, but poorer appearance and 3D structure. While these models have complementary strengths, they also have opposing weaknesses, making it difficult to combine them in a way that alleviates this three-way tradeoff. Here, we introduce hybrid score distillation sampling, an alternating optimization procedure that blends supervision signals from multiple pre-trained diffusion models and incorporates benefits of each for high-fidelity text-to-4D generation. Using hybrid SDS, we demonstrate synthesis of 4D scenes with compelling appearance, 3D structure, and motion.
1. Introduction
Text-to-4D generation builds on diffusion-model priors but must balance appearance, 3D structure, and motion. 4D-fy addresses this tradeoff with hybrid supervision from complementary pretrained models.
- Motivation: Text-to-4D generation draws on image- and video-diffusion priors for applications in augmented and virtual reality, computer animation, and industrial design.The broader generation advances are enabled by internet-scale image–text datasets and diffusion models, while video generation remains constrained by limited video-dataset scale.
- Background: Text-to-4D generation uses diffusion-model supervision to iteratively optimize a scene representation from rendered, noised images.This procedure, called score distillation sampling (SDS), estimates denoising gradients to update the 3D representation.
- Problem: Existing text-to-4D methods face a three-way tradeoff among appearance quality, 3D structure, and motion.The tradeoff depends on whether SDS uses text-to-image, 3D-aware text-to-image, or text-to-video models.
- Problem: Text-to-image models can produce realistic appearance, but multiple viewpoints may expose incorrectly repeated structures known as the Janus problem.3D-aware diffusion models improve structure by modeling camera viewpoints, but require synthetic posed-image fine-tuning that sacrifices appearance quality.
- Contribution: 4D-fy introduces hybrid SDS, which blends gradient updates from multiple pretrained diffusion models through alternating optimization.The method aims to combine complementary strengths across appearance, 3D structure, and motion and reports state-of-the-art text-to-4D results.
3. Method
4D-fy represents dynamic scenes with separate static and dynamic multiscale hash features, rendered through volumetric neural rendering. Its hybrid SDS procedure alternates supervision from 3D-aware image, image, and video diffusion models across staged optimization to improve structure, appearance, and motion.
- 4D Neural Representation: Volumetric rendering casts rays through sampled 3D points, retrieves density and color, and alpha-composites them into rendered pixels.The representation is queried with position and time for dynamic scene rendering.
- 4D Neural Representation: The 4D representation uses static and dynamic multiresolution hash tables, with a time input enabling time-varying density and color.Static and dynamic features are combined and decoded into density and color by MLPs.
- Hybrid Score Distillation Sampling: Hybrid SDS combines 3D-aware SDS for structure, VSD for appearance, and video SDS for motion during alternating optimization.The three supervision signals are smoothly merged rather than applied as a single objective.
- Optimization Procedure: Stage 2 alternates 3D-aware and image-model gradients, improving appearance while retaining freedom from the Janus problem.Each iteration randomly selects between the two update types using their specified probabilities.
4. Experiments
4D-fy is evaluated against MAV3D using CLIP Score, human preference, qualitative renderings, and component ablations. The results show stronger appearance, 3D structure, and text alignment, comparable motion, and benefits from hybrid SDS and the hash-grid representation.
- Results: 4D-fy produced realistic renderings across space and time, whereas MAV3D noticeably lacked detail.The comparison used spatio-temporal renderings and depth maps.
- Results: 67% of users preferred 4D-fy over 33% for MAV3D overall.Users also significantly preferred 4D-fy for appearance quality, 3D structure quality, and text alignment, while motion quality was roughly on par.
- Metrics: 4D-fy outperformed MAV3D in CLIP Score and overall user preference across the evaluation.The evaluation compared the methods using CLIP Score and human preferences for appearance, 3D structure, motion, text alignment, and overall preference.
- Ablations: Removing image guidance caused failures in both realistic appearance and 3D structure for dynamic scene generation.This ablation trained directly with text-to-video guidance by skipping the first two stages.
- Ablations: Removing 3D-aware guidance preserved similar motion and high-quality appearance but degraded 3D structure.The degradation appeared for both scenes shown in Figure 5.
- Ablations: Hybrid SDS preserved realistic appearance and high-quality 3D structure by combining text-to-image, 3D-aware text-to-image, and text-to-video guidance in the final stage.Disabling these image-guidance updates in the third stage produced degraded appearance and 3D structure.
5. Conclusion
4D-fy synthesizes high-quality 4D scenes using hybrid score distillation sampling and alleviates the tradeoff among appearance, 3D structure, and motion. The method is limited to simple movements and leaves richer metrics and faster generation for future work.
- 4D-fy synthesizes high-quality 4D scenes from text prompts using hybrid score distillation sampling.
- The method alleviates the three-way tradeoff among appearance, 3D structure, and motion.
- Motion complexity is limited to simple movements.
- Current text-to-3D metrics rely mainly on image-based metrics and user studies, motivating more sophisticated 3D and 4D metrics.
- Generating each scene takes a significant amount of time.
S1. Implementation Details
The implementation uses a hash-grid representation, staged diffusion optimization, and NerfAcc rendering. Training requires substantial computational resources, while frame rendering is close to that of a static 3D model.
- Diffusion timesteps are annealed during the first 5000 iterations, while video SDS uses the broader range td ∈[0.02, 0.98] in later stages.
- The representation uses 16 levels, two features per level, and base resolution 16 for both static and dynamic components.
- NerfAcc renders with 512 samples per ray; inference samples 64 frames after training with 16 frames.
- Training uses roughly 80 GB of VRAM, with the three optimization stages requiring approximately 2, 2, and 19 hours.
- 71ms per frame for 4D-fy is close to 68 ms for a static 3D MVDream-based model.
S2. User Study
The user study compares 4D-fy with baseline and ablated methods across appearance, structure, motion, text alignment, and overall preference. Users significantly preferred 4D-fy over MAV3D on most evaluated properties, while motion quality was roughly comparable.
- The survey contains 53 questions, each with five subquestions comparing videos generated from the same text prompt.
- Evaluators assess appearance quality, 3D structure quality, motion quality, text alignment, and overall preference.
- The study compares 4D-fy with MAV3D in 28 questions and with five ablated methods in 25 questions.
- Statistically significant results favored 4D-fy over MAV3D, except for motion quality.
S3. Qualitative Results
The supplementary material presents additional 4D-fy results. The authors direct readers to the supplementary website to view the videos and better assess the generated scenes.
- Figure S7 presents additional results generated using 4D-fy.
- The supplementary website includes videos of the text-to-4D generation results.
- Viewing the included videos is encouraged to better appreciate the generated results.
S4. Ablations
The ablations show that jointly applying all three diffusion models only in the final stage performs poorly, while decomposed static and dynamic hash grids improve quality.
- Naively training only the last stage with all three diffusion models leads to poor-quality results.The model struggles to learn 3D structure, appearance, and motion simultaneously.
- Using one large dynamic hash grid instead of decomposed smaller static and dynamic hash grids produces significantly lower-quality results.
S5. Geometry
The extracted normals and meshes indicate that 4D-fy produces geometry comparable to previous text-to-3D methods, although high-quality geometry is not its primary goal.
- 4D-fy produces normals and meshes with geometry comparable to previous text-to-3D methods.The geometry is extracted using marching cubes.
S6. Limitations
4D-fy currently generates only simple movements and can exhibit temporal flickering, with stronger video supervision potentially trading image quality for reduced flickering.
- The complexity of motion in 4D-fy scenes is limited to simple movements.The authors point to future progress in text-to-video generation as a potential source of improvement.
- Temporal flickering can occur in 4D-fy renderings, and emphasizing video-diffusion supervision may reduce it at the cost of image quality.Avoiding blurry or overly smooth images while reducing flickering remains future work.
S7. Ethics Statement
The paper states that the current method cannot edit real people and condemns its use for harmful realistic fake content or misinformation.
- In its current form, 4D-fy cannot edit real people.The authors note that the method could nevertheless be extended and misused for edited imagery of real people.