BrightDreamer: Generic 3D Gaussian Generative Framework for Fast Text-to-3D Synthesis
Authors: Lutao Jiang
Xu Zheng
Yuanhuiyi Lyu
Jiazhou Zhou
Lin Wang
Venue: Accepted by TMLR ()

Abstract: Text-to-3D synthesis has recently seen intriguing advances by combining the text-to-image priors with 3D representation methods, e.g., 3D Gaussian Splatting (3D GS), via Score Distillation Sampling (SDS). However, a hurdle of existing methods is the low efficiency, per-prompt optimization for a single 3D object. Therefore, it is imperative for a paradigm shift from per-prompt optimization to feed-forward generation for any unseen text prompts, which yet remains challenging. An obstacle is how to directly generate a set of millions of 3D Gaussians to represent a 3D object. This paper presents BrightDreamer, an end-to-end feed-forward approach that can achieve generalizable and fast (77 ms) text-to-3D generation. Our key idea is to formulate the generation process as estimating the 3D deformation from an anchor shape with predefined positions. For this, we first propose a Text-guided Shape Deformation (TSD) network to predict the deformed shape and its new positions, used as the centers (one attribute) of 3D Gaussians. To estimate the other four attributes (i.e., scaling, rotation, opacity, and SH), we then design a novel Text-guided Triplane Generator (TTG) to generate a triplane representation for a 3D object. The center of each Gaussian enables us to transform the spatial feature into the four attributes. The generated 3D Gaussians can be finally rendered at 705 frames per second. Extensive experiments demonstrate the superiority of our method over existing methods. Also, BrightDreamer possesses a strong semantic understanding capability even for complex text prompts. The project code is available in supplementary materials.

Published in Transactions on Machine Learning Research (03/2026)
BreahrDreamer: Generic 3D Gaussian Generative Frame- work for Fast Text-to-3D Synthesis
Lutao Jiang
Thrust of Artificial Intelligence The Hong Kong University of Science and Technology (Guongshou)
Xu Zheng
Mhenng@28fftommmcc...b.ss.ggggg edu.cc
Thrust of Artificial Intelligence The Hong Kong University of Science and Technology (Guongshou)
Yuanhuiyi Lyu
Thrust of Artificial Intelligence The Hong Kong University of Science and Technology (Guangshou)
Jiazhou Zhou
jahou2g7@oonneet www.s:/wwtttt.ttttttoooeeeeeeeee edu.co
Thrust of Artificial Intelligence The Hong Kong Universily of Science and Technology (Guangzhou)
Lin Wangt
Browang www.uunngnnuu......... edu.sg
School of Electrical and Electromic Engineering Nanyang Technological University
Reviewed OpenReview: /ttps://openrenee net/ formm? id= Rb190UCCtub
Abstract
Text-to-3D synthesis has recently seen intriguing advances by combining the text-to-image priors with 3D representation methods, e.g., 3D Gaussian Splatting (3D GS), via Score Distillation Sampling (SDS). However, a hurdle existing mothods the low efficiency, per-prompl optimization for a single 3D object. Therefore, it imperative for a paradigı shift from per-prompt optimization to feed-forward generation for any unseen text prompts, which yet remains challenging. An obstacle is how to directly generate set of millions of 3D end-to-end
Gaussians to represent 3D object. This paper presents BrightDreamer, feod-forward approach that can achieve generalizable and fast (77 ms) text-to-3D genera- tion. Our key idea is to formulate the generation process as estimating the 3D deformation from an anchor shape with predefined positions. For this, we first propose Text-guided Shape Deformation (TSD) network to predict the deformed shape and its new positions, usod as the centers (one attribute) of 3D Gaussians. To estimate the other four attributes (i.e., scaling, rotation, opacily, and SH), we then design novel Text-guided Triplane Gen- erator (TTG) to generate triplane representation for 3D object. The center of each Gaussian enables us to transform the spatial feature into the four attributes. The generated 3D Gaussians can be finally rendered at 705 frames per second. Extensive experiments demonstrate the superiority of our method over existing methods. Also, Bright possesses a strong semantic understanding capability even for complex text prompts. The code is available in the supplementary materials.
Corresponding author.

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Published in Transactions on Machine Learning Research (03/2026)
1 Introduction
Text-to-3D generation has received considerable at- tention in the computer graphics and vision commu- nity owing to its immersive potential across diverse applications, such as virtual reality and video gam- ing (Li et al.) 2023a). Recently, with the emergence of diffusion models (Ho et al. 2020 Rombach el al.)
Distill the Knowledge of Only One Single text Diffusion Model
Target
S
update
update
photo of hancburger"
SDS Loss
Render
Only for single text
3DGS Model
2022) and neural rendering techniques (Mildenhall et al. nessed umprecedented technical advancement. In
(a) Per-Prompt Optimization
2021 Kerbl et al. 2023). text-to-3D has wit-
Distill More Knowledge
Target
Diffusion Model
particular, pioneering methods, such as DreamFu- sion (Poole et al. [022), LatentNeRF (Metzer et al., 2023). SJC (Wang et al.] 2023ap, have sparked sig- nificant: interest in the research community, catalyz- ing trend toward developing techniques for cre- ating 3D assets from texts. The follow-up methods then focus either quality improvement (Raj et al.)
Vehicle
3DGS Generator
Daily Life
Animal
77ms
Any text
Inference Only
3DGS Models
(b) Feed-forward Generation (Ours)
2023
Shi et al.]
2023: Wang et al.; 2023b; Liang
Figure 1:
A comparison between per-prompt optimization- based methods, and our feed-forward generation-basen approach with an end-to-end objec- tive.
et al.
2024) or geometry refinement (Chen et al.,
2023a:
Lin et al.]
2023) or training efficiency (Tang
et al.
2023
Yi et al. 2023
The dominant paradigm of these methods to randomly initialize a 3D representation model, e.g., Neural Radiance Fields (NeRF) (Mildenhall et al. 2021) or 3D Gaussian Splatting (3D GS) (Kerbl et al. 2023), 3, and optimize such a model to align with a specific text prompt, as depicted in Fig. (a). Unfortunately, these methods suffer from two critical constraints. Firstly, as per-prompt optimization usually requires several tens of thousands of iterations, this inefficiency brings a considerable obstacle to broader applications. It. is significantly different from the mainstream training paradigm in the field of 2D image generation (Song it al. 2020 Rombach et al.] 2022; Ramesh et al. 2022) or 3D-aware image generation (Schwarz et al.] 2020
Chan al. 2021; Jiang et al., 2023 Or-El et al. 2022 Chan et al. 2022): a generative model trained with collection of text-image pairs or images, and the model can generate the desired content from any input at the inference stage. Secondly, as shown in Fig. 2 (a), existing methods often fail to accurately process the complex lexts. For example, the mainstream methods struggle in generating 3D content that input prompt contains complex interaction between multiple entities, This limitation arises from the models being trained on a single text prompt, resulting in a degraded capability in comprchensive semantic understanding.
2(a),
Therefore, it is urgently needed for a paradigm shift from per-prompt optimization to develop a generic texi- to-3D generation framework. Once trained, the generative framework should be able to generate content from any text prompts in the inference stage, depicted in Fig. [b). Furthermore, given the scarcity of 3D data in comparison to the abundance of 2D image data, leveraging well-trained 2D image diffusion models to facilitate the training of 3D generative models presents more effective and resource-efficien approach. Previously, some research efforts, e.g., ATT3D (Lorraine et al.) 2023) and Instant3D (Li et al. 2024ch, have been made grounded in NeRF representation. The core insight is to add a large mumber of texts and take them as conditional inputs to generate explicit spatial representations, such as triplane (Chan ct al.] 2022). Nonetheless, in stark contrast to the volume rendering in NeRF, 3D GS representation for an object usually consists of millions of 3D Gaussians. Consequently, there exists an inherent and natural difficulty in converting the generation representations into 3D GS ones in their framework.
In this paper, we propose BrightDreamer, an end-to-end feed-forward framework that, for the first time, can achieve generalizable and fast. (77 ms) lext-to-3D GS generation. Bright exhibits a robust ability for complex semantic understanding (Fig, 2 (a)), and it demonstrates a substantial capacity for gencralization (Fig. 2 (b)). Additionally, like traditional generative models (Goodfellow et al. 2014),, our generator can interpolate between two inputs (Fig, 2 (c)), allowing users to unleash their imagination and creativity, thus expanding the potential for novel and nuanced design exploration. As stated before, the
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DreamGausstan
Saturation Change Color Change
"Electric lourry SUV, light yellow, spacious advanced tech
X
LucidDreamer
"Electrcc koury SUV, deep purple spacious advanced tech
Ours
man wearing hat mowing woman frying on a dress"
"Electric huury SUV, light purple spacious advanced tach"
(a) Complex Semantic Understanding
(b) Detail Understanding
"Blectric hunuyy SUV, deep purple, spacious,
houry SUV, light yollow, spacious,
*Vintage pickup, sky blue, nigged appeal classic functionality"
'Electric lonny SUV, light yollow, spacious, advanced tech"
(c) Flexible Interpolation between two prompts
Figure 2: DreamGaussian (Tang et al. 2023 and LucidDreamer Liang et al. 2024) are both optimized for a single text. Our result is the direct generation. And for the display of our generalization, all the prompts do not appear in our training set. (a) is for showing the complex text understanding, (b) is to demonstrate our capability of understanding details. is noteworthy that light purple, deep purple, and light yellow don't appear in the training set. (c) Interpolation between two prompts from color and shape perspectives.
3D GS representation of an object usually comprises several hundreds of thousands of 3D GS elements, Thus, directly generating such a large collection is impractical. Our key idea is to redefine this generation problem as its equal problem, i.e., 3D shape deformation. Specifically, we place and fix some anchor positions form the initial shape. Then, can be deformed the desired shape by giving different input prompts through our designed Text-guided Shape Deformation (TSD) network (Sec. [3.I]. Then, the new positions be set to the centers of the 3D Gaussian. Upon establishing the basic shape, we elaborately design Text-guided Triplane Generator (TTG) to generate spatial representation of the 3D object (Sec. 3.2 Subsequently, we utilize the spatial feature of each center of 3D Gaussian to represent its feature and translate it into the remaining attributes (including scaling, rotation, opacity, and SH coefficient) through our well- designed Gaussian Decoder (Sec. 3.3 For TTG, based on our re-analysis the previous convolution- based triplane generation process, we identified and solved two primary deficiencies. One issue involves the spatial inhomogeneity observed during the calculation process, as shown in Fig. The other issuc ariscs from the single-vector style control mechanism similar to StyleGAN (Karras al. 2019 which complicates managing relationships between multiple entities.
Our contributions can be summarized follows: (I) We propose BrightDreamer, the first 3D Gaussian generative framework to achieve generalizable and fast texl-to-3D synthesis. (II) We design the Text-guided Shape Deformation (TSD) network to simplify the difficulty direct generation 3D Gaussians. We design the Text-guided Triplanc Generator (TTG) to generate the object's spatial features and then docode them the 3D Gaussians. For TTG design, we re-analyze and solve the existing problems in the mainstream triplane generator, including spatial inhomogeneity and text understanding problems. (IIT) Extensive experiments
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demonstrate that BrightDreamer not only can understand the complex semantics (while the per-prompt optimization methods fail) but also can utilize its generalization capability to achieve generation control.
2
Related Works
Existing methods can be grouped into two categories. 1) Optimization- methods typically commence with randomly initialized 3D model, such as NeRF (Mildenhall et al. 2021], and subsequently employ text-image priors (Radford et al., 2021 Rombach et al. 2022) to guide and optimize its parameters. After undergoing thousands of iterative refinements, this predefined 3D model progressively morphs to embody the shape described by the corresponding text imput. Dream Field (Jain) et al.] 2022) represents the inaugural foray into text-to-3D methodology, utilizing the pre-trained text-image model, CLIP (Radford et al. 2021]; ) as a guiding mechanism for the optimization process of predefined NeRF model. DreamFusion (Poole et al. 2022 proposes the Score Distillation Sampling (SDS) to transfer the prior of the 2D diffusion model (Ho et al. 2020) into 3D representation model (Mildenhall el al. 2021
Text-to-3D Generation.
2022). which achieves impressive performance and ignites the research enthusiastic for the text- Müller et al.
2023b 2024 Liang ct al. 2024 Tran ct al. 2023 to-3D task. Inspired by this, numerous works (Wang et al.
Yu al.
Li et al. 2025 2024a Yang et al. 2024b Wu et al. 2024 are devoted to re-designing the SDS loss,
enabling much more local details of the 3D model. MVDream (Shi et al., 2023) and PerpNeg (Armandpour
et al. 2023) attempt to solve the Janus problem, i.e., multi-face problem in some text prompts. Though these methods have great generalizability, it usually needs several hours to optimize a 3D model. 2) Gencration- based methods, by contrast, aim to directly generale 3D model from a given lext, streamlining the process of text-to-3D generation. ATT3D (Lorraine et al.) 2023 is the first attempt to train a NeRF model with multiple texts by SDS. Though its generalizability is limited, benefitting from SDS, the rendered image
2019 Fu et al.) 2022 Liu et al. 2022 is better than previous methods (Sanghi et al.) 2022 Chen et al.
and the generated content is not limited to 3D data (Mittal et al.] let al.] 2024ch designs some modules map the text input to the EG3D model (Chan et al. 2022 and
2022 Sanghi et al.) 2023 Inst.nnt (Li
then uses SDS to train this model. Concurrently, there are also some works focusing text-to-image go-3---D---DDD
model route [Xu et al.
2024d
2025 Lu et al.) 2024 Wei et al.) 2024 Xu et al.) 2024b Wang 2024 Yang et al.]
Tang et al.
ct al. 2025
Liu et al.
2024a Hong et al. 2023 Zou et al.] 2024 Li et al. He et al.
2024 Xu et al. 2024c). However, due to the limited amount of 3D datasets, it is extremely hard to train
a generic model to implement this target well (Sec. [.2.4). Different from the mentioned works, we propose BrightDreamer, a generic framework that, for the first time, can achieve fast (77 ms) direct text-to-3DGS generation. Morcover, our method doesn't depend on 3D data, which shows a greater potential.
3D Gaussian Splatting (GS). Recently, 3D GS (Kerbl et al. 2023 has emerged as the leading method for 3D representation method of 3D objects scenes, offering faster rendering speeds and greater application potential than NeRF (Mildenhall et al. 2021). In a short time, large number of methods have been proposed to leverage 3D GS for diverse tasks, e.g. anti-aliasing novel view synthesis (Yu et al.] 2023 Yan
et al. 2023b), SLAM ( (ann et al. 2023a Keetha et al. 2023 Matsuki et al. 2023 Yugay et al. 2023), human
reconstruction Li et al.] 2024b Moreau et al. 2023: Kocabas et al.
2023 Abdal et al.
2023
Li et al.
2023b
Liu et al. 2023). dynamic scene reconstruction (Luiten et al. 2023
Yang al.
2023b
Wu et al.
2023 Yang
et al.
2023a Xu et al.] 2023), and 3D content generation [Xu et al.
2024a Chen et al. 2023b Tang et al.
2023
Yi et al. 2023 Li et al. 2023c Liang et al.
2024). Our work builds on 3D GS, aiming to develop
generic
text-to-31 3D generation framework that can generate 3D GS with low latency (77 ms).
3 Method
Briiht aims to generate 3D Gaussians directly from text prompts. After training, it can generate the 3D GS model with a remarkably low generation latency (about 77ms). And, the generated 3D Gaussians can be rendered at an impressive inference speed of over 700 frames per second. Each 3D Gaussian is defined by five attributes: center p', scaling S, rotation R, opacity a, and SH coefficient SH. Our key idea is two-fold: 1) Defining anchor positions, i.e., prodefined positions, to estimate the center of 3D Gaussians; 2) Building implicit spatial representation, which can be decomposed to estimate the other four attributes of 3D Gaussians.
Overview.
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(a)
Add Norm f
(b)
X3
Text
SDS Loss
Text-guided
Text-guided Triplone Generator (Sec. 3.2)
Shape Deformation
Feed
Conv
View
Splatting
Network (Sec. 3.1)
Add Norm
Forward
Upsample Block
X5
(c)
Gausslans
Gaussian
Decoder
(Sec. 3.3)
Muati-Head
Attention
Spatial Transformer
X2
K,V
K.V
Mean
Add & Norm
ResConv
Feed Forward
Block
77 1024
77 1024
Add Norm
Spatial Transformer
X3
K
CLIP Text Encoder
Multi-Head Attention
CLIP Text Encoder
X-Y
X-Z
Y-Z
K,V
ResConv Block
Norm
1Q
2 with teari and wsering feather juckef ridings metaregsle
N
3
with keard and weering teather jucket riding matoreyele
2D Position Query
Nx3
Anchor Positions
Figure An overview of BrightDreamer. The details of Spatial Transformer, ResConv Block and Upsample Block are shown in Fig.
Intuitively, we propose BrightDreamer, and an overview is depicted in Fig. 3 Given a text prompt as input, we transform network (Sec. 3.1 transforms the fixed anchor positions to the desired shape with text guidance. The new positions used as the centers of 3D Gaussians. We then design the TTG (Sec. 3.2) to separately generate three feature planes to construct the implicit spatial representation. Based on the centers of Gaussians, we can obtain their spatial features, which are then transferred to the other attributes through the Gaussian Decoder (Sec. 3.3 Finally, we render 3D Gaussians to 2D images and use the SDS Loss (Poole et. al. 2022) to optimize the whole framework. We now describe our Briahe detail.
a 77 1024 embedding through the frozen CLIP text encoder. Next, the TSD
3.1 Text-guided Shape Deformation (TSD)
The goal of TSD is to obtain the center (one attribute) of each 3D Gaussian. Considering that directly outputting a huge number of center coordinates is extremely difficult, we overcome this hurdle by deforming the anchor positions instead of generating them.
Anchor Position. The anchor positions are predefined positions, which are the fixed coordinates. serves one of the inpuls for the TSD. Specifically, we place the anchor positions on the vertices of 3D grid, represented by the gray points in Fig. 3 Then, design the TSD network to predict their deviation to deform the initialized shape of the 3D grid, guided by the text prompt input.
Network Design. As shown in Fig. (a), the inputs of TSD are text prompts and anchor positions. Firstly, the text prompts are encoded as the text embedding by an off-the-shelf text encoder, e.g., CLIP (Radford ct al. 2021] or T5 (Raffel al. 2020). Considering the possibility of the complex input sentence, it remains non-trivial how bridge each position and word in the sentence. The cross-attention (Vaswani et al. 2017) quantify the correlation degree between each point and each word within sentence. We then employ the cross-attention to design module to obtain the deviation from the anchor position. It consists of the Layer Normalization [Ba et al. 20166,, Multi-Head Attention, Feod-Forward Network, and shortcut connection (Vaswani et al.) 2017). Consequently, certain positions, correlating more closely with corresponding words the sentence, are assigned with higher attention scores. This process enables the aggregation of features that more accurately reflect the characteristics of the corresponding words. The detailed computation process is formulated as follows:
W Wo(n)Wx(y), Wv(v)),
output = (50
(1)
Vd
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where p E R is the 3D coordinate of the anchor position, y E I27 1024 is the text embedding of the imput prompt, 77 and 1024 are the sentence length and the embedding dimension, Wo(+), Wx(+) and (v(+) are the query, key, value transformation function, d is the feature dimension, (-) is the feed-forward network. The output of the TSD network is the offsct A E R of the anchor positions. To ensure the stability of the training, we control the maximum extent to which a point can deviate from the anchor position. Specifically, given the degree of freedom 3 E IR, we use the following equation to adjust the range of output into interval (-B,8):
2 B outpot) 8.
(2)
Finally, the deformed position p' E RR3, which represents the centers of 3D Gaussian corresponding to input prompt, is formulated as follows:
y' = p A.
3.2 Text-guided Triplane Generator (TTG)
After determining the centers of the 3D Gaussians, we need to obtain the other four attributes. To efficiently assign features to each Gaussian, the objective of TTG to generate an implicit spatial representa- tion in space, represented by the triplane. Thus, we design novel and highly efficient triplane generator that takes text prompts as input.
One challenge is that the previous triplane generation approaches, such as EG3D (Chan et al. 2022 and Instant3D (Li et al., 2024c), exhibit the problem of spatial inhomogeneity, as shown in Fig. 4 Since they directly segment a leature map into three feature maps along the channel dimension, only a few areas are computed together. For example, the position (0, 0) in the 2D space is unfolded to (0, 0, :), (0, ;, 0), and (:, 0, 0), denoted by blue color in Fig. Taking 1 x Cone as an example, only these three arcas are calculatod logether. On the contrary, (0, 0, :) is hardly possible to be calculated with (0, :, 1), because they do not appear at the same pixel in the 2D feature map. The same applies to the 3 x 3 Cone. This means that only few areas share the same spatial information, while the others do not, thus causing spatial inhomogeneity. For this, a simple yet effective way is to apply three generators (without sharing weights).
Another challenge is that given the complex prompts, squeezing a sentence into a single style feature vector to apply AdaIN (Karras et al.) 2019 2020 2021) could result in loss of local details. Therefore, need more fino-grained generation method guided by the word level, thus can relain more information of text encoder trained on large- scale datasct. Naturally, calculating cross-attention between the pizels of the feature map and words in the sentence is a better choice.
(a) Convolution kernel (0,0)
(b) Convolution kernel (0.1)
(c) Convolution kernel at (0,2)
Figure 4: The visualization of expanding 2D 1 con- volution kernel (blue area) to 3D and its moving pro- coss in previous convolutional triplane generator Chan et al. (002)).
To address these two challenges, we design the Text- guided Triplane Generator (TTG), shown in Fig. 3 (b). Our TTG is designed with the inspiration from the spatial transformer block and residual convolutional block in Stable Diffusion (Rombach et al., 2022 Considering the increased computational demand associated with pixel-wise self-attention in the feature map of (Rombach et al. 2022), we do not incorporate this layer into our network. Instead, we find that interleaved convolutional layers can sufficiently facilitate the interaction within the feature map. The detailed designs are shown in Fig. 5 For the whole pipeline, we first initialize a 2D input query according to its 2D trigonometric function position encoding. Three ResConv Blocks and Spatial Transformer blocks are stacked to assemble the prompt word features at low resolution through cross-attention. We then gradually increase the resolution of the feature map through the stacks of ResConv Blocks, Spatial Transformer blocks, and Upsample Block by five times, as depicted in Fig. 3 (b). Finally, we use a Conv layer to output the plane feature. We describe the design of the Spatial Transformer block, ResConv block, and Upsample block in detail.
Spatial Transformer Block. As Fig. 5 (a) shows, the Spatial Transformer Block comprises two multi- head cross-attention modules and a ferd-foowward network (Vaswani et al. 2017). The process is initiated
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by flattening the 2D feature map into a 1D structure, thereby transforming the dimensions from (H, W, C) to (H x W, C), with each pixel's feature considered as the input query embedding. Subsequent to this transformation, the features undergo normalization via Layer Normalization (Ba et al. 2016). The normal- ized features serve as querics, while the text embeddings act as keys and values in the computation of the cross-attention feature. This cross-modality attention mechanism is designed to align the feature map with the corresponding words in the input sentence. Following the application of two cross-attention modules, the features are further refined through a feed-forward network. This sequence of operations also incorpo- rates the use of skip connections, mirroring the original transformer architecture (Vaswani et al. 2017], to facilitate effective feature processing and integration.
Res Conv Block. As shown in Fig. 5 (b), the Resid- ual Convolutional Block includes a Layer Normalization layer, a SiLUU activation function (Elfwing et al. 2018)) and 3 x convolutional layers, with the skip connection between input and output.
Add Norm
Conv3x3
Interpolate
Feed Forward
Feature Map
Add & Norm
(c) Upsample Block
Upsample Block. As depicted in Fig. 5 (c), the Up- sample Block begins with interpolating the feature map to enlarge it by a factor of 2x, followed by processing through a x 3 convolutional layer.
K.
Multi-Head Attention
Add
Norm
Conv3x3
K.V
Multi-Head Attention
SILU
3.3 3D Gaussians Decoder
Q
Norm
Norm
Conv3x3
We aim to obtain four additional attributes of 3D Gaus- sian necessary for generation. Once the triplane, consist- ing of the three feature planes Tays Tass Tya is generated, we can obtain the feature vector F E R of each Gaus- sian based on its center p'. This feature vector is then converted into the additional attributes, including opec- ily a E IR, scaling S E RR3 rolation R R1, and 1-order SH coefficient.
Text Embedding
Flatten
SILU
Norm
Feature Map
Feature Map
(a) Spatial Transformer Block
(b) Residual Convolutional Block
Figure 5: A detailed illustration of spe- cific blocks. (a) Spatial Transformer Block. (b) Residual Convolutional Block. (c) Upsam- ple Block.
Specifically, we first project the 3D coordinate onto three planes, X-Y, X-Z, and Y-Z. Based on the projected 2D coordinates, we can derive the features Fry, Fzz, Fyee according to the interpolation with their four vertex in the 2D feature maps. To ensure the gradient back- propagation is evenly distributed across all three planes, we utilize an averaging operation to aggregate these fea- tures, thereby obtaining the 3D Gaussian's 's feature F. Given that the attributes of 3D Gaussian can be categorized into two groups, i.e, shape and color, we develop two distinct transformation modules F Fahape and Feotor- Each module is a lightweight, two-layer Multi-Layer Perceptron (MLP) network. To enhance the gradient back-propagation to the TSD, the center 3D Gaussian p' is additionally inputted into both modules:
a, S, R = Fshape (F,D), SH = Feotor Footor(F,,'))
(4)
In the training process, we observe the scaling S is not a stable variation, and the memory consumption of 3D Gaussian rendering is extremely sensitive to it. Therefore, we use the following equation to control it to interval (a,b):
S (b a) sigmoid(S) S) + a.
(5)
Upon obtaining all attributes of 3D Gaussian, our generation process is completed. We can render it from arbitrary view direction to 2D images.
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3.4 Optimization
Our Training commennces with the selection of B prompls from the training set. These prompls are then fed into our 3D Gaussians generator, which is tasked with generating the corresponding 3D GS representation of the objocts. Following this, we proceed to randomly sample C view directions to render C 2D images. The B C rendered images are supervised through the Score Distillation Sampling (SDS) loss function (Poole et al. )022), as Fq. shows, conjunction with the Perp-Neg (Armandpour et al. 2023). In this way, our generator can gradually construct mapping relationship between text and 3D.
Vollo, =0o()) - Ex,e (u(() (&o (2iss' ())-())-)))) e) D0
0x
66)
where y is the input prompt embedding of the generator, & denotes the trainable parameters of 3D Gaussians generator, & denotes the parameters of denoising network, x the generated image by process go(:), w((t) is the weighting function with time step in the denoising schedule, e is the random noise, 2t is the noisy latents encoded from x, y is the input prompt, and y' adjusted text according the sampling view direction, (-(-) is the predicted noise.
4 Experiments
This section first outlines the implementation details, inference speed, parameter count, and training dataset. The following comparison experiments focus on four aspects: (I) comparison with per-prompt text- to-3DGS, (II) fine-tuning the generated 3DGS model and then comparing with per-prompt text-to-3DGS, (II) comparison with amortized text-to-NeRF, (TV) comparison with mext-to-image-t--3DGG methods. Finally, we present interesting findings regarding unseen words and conduct ablation studies to validate the network design. It's worth noting that all prompts in the experiments don't appear in the training dataset.
4.1 Implementation Details
Our codebase is constructed on the PyTorch frame-
Table The generation latency (millisccond) and ren- dering speed (FPS, Frames Per Second).
work (Paszke et al., 2019 with Automatic Mixed
Precision Training (AMP) and Gradient Check-
pointing technology (Chen et al.
2016)- We the 2014 with con-
Device
Generation Latency Rendering Speed
Adam optimizer [Kingma & Ba,
RTX 3090 24GB A800 S0GB
79 ms
698 FPS
stant learning rate of 5 x 10-5, B1 of 0.9 and B2 of 0.99. We train our generator using the DeepFloyd IF (Stabiiiity 2023) UNet to calculate the SDS Loss (Eq. camera batch size is set to 4. All our experiments are conducted on a server with GPUs with 80GB memory. We set the freedom 3 (Eq. 2 to 0.2, and the range of scaling (a, b) (Eq. 5 to (-9, -3). The anchor position is placed as 64 3D grid, and the resolution of the generated triplane is 256 256. The generator is trained single prompt set including vehicle, daily life, and animal descriptions, in total of 30K sentences. In Tab. 1 we show the inference latency on a single A800 GPU and a single RTX3090 GPU, which shows large margin improvement, compared to optimization-bart based methods, which need several hours. Our generator contains about 500M trainable parameters. We provide the training and inference pscudo code in supplementary materials.
77
705 FPS
The prompt batch size is set to 64 and the
4.2
Comparison with other methods
We compare our method with previous Lexi-to-3DGS methods. The comparison experiments setting includes four aspects: (I) comparison with per- per-prompt text-to-3DGS, including DreamGaussian (Tang et al. 2023), GSGEN (Chen et al.) (202bb) and LucidDreamer (Liang al.) (2024) (II) fino-tuning the generated 3DGS model, and then compare with per-prompt text-to-3DGS, (III) comparison with amortized text- to-NeRF, including Instant3D (Li et al. 2024c), (IV) comparison with text-to-image-oo-3DGGS methods.
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5 min DreamGaussian
hours GSGEN
2 hours
77ms Ours
5min Ours-Finetune
Instant3D
Ours-Finetune Instant3D
Ours-Finetune
LucidDreamer
"Luxury roodster, sky blue, unmatched elegance, superior driving"
"a beorded man wearing backpock is climbing a mountoin"
"A handsome man wearing leather jacket is riding motorcycle"
"Racing sedan, silver mist, cleon energy propuision. edvanced safety"
"a muscular is exercising fitness room"
"an elderly and woman trimming her plonts"
woman wearing backpack is climbing mountain"
Figure 7: Comparison with amortized text-to-NeRF method.
Figure 6: Comparison with per-prompt methods.
including LGM Tang et al. 2025 and GRM Xu et al. 2024d) Besides, we present the quantitative results of human preference.
4.2.1 Per-prompt Text-to-3DGS
We seloct the current SoTA open-source text-to-3DGS methods, including DreamGaussian (Tang et al. 2023), GSGEN (Chen et al. 2023b) and LueidDreamer (Liang al. 2024] for comparison. In this setting, directly imput the text prompt to BrightDreamer and the same prompt to optimize the 3D GS model in other methods. As shown in the first four columns of Fig, 6 the other methods are hard to generate semantic-aligned 3D GS models from complex text prompts, primarily due to their limited capability in comprehending intricate text. imputs.
similar conclusion can be found in the works (Jiang et al. 2024 Zhou et al. Cheng et al. Bai et al., 2023 Epstein et al.). is hard to optimize a 3D GS model for a prompt containing multiple objects. Our method, trained across diverse distribution of multiple text prompts, exhibits robust capability to comprehend complex prompts effectively.
4.2.2 Quickly Finetuning after generation
We demonstrate that the 3D GS model generated by BrightDreamer be efficiently fine-tuned using per-prompt optimization methods to enhance qual- ity. Specifically, we use the ISM (Liang et al.] 2024 loss to optimize the generated 3D GS model for sev- cral hundred iterations. As evidenced in the last col- umn of Fig. [ the third and sixth columns of Fig. 7 the model's quality of details significantly improves within just several minutes.
"Electric luxury SUV, apple spocious, tech"
"Fomily minivan, yellow, large copocity, economicol"
4.2.3 Amortized Text-to-Nett
We compare Briahe with SoTA open-source amortized Text-to-NeRF methods, Instant3D (Li et al. 2024ch. It is worth noting that our method Figure 8: Comparison with text-to-image-to-DDGS is the first amortized Text-to-3DGS method. More- methods. over, our method inherits numerous advantages of 3D GS over NeRE, Since ATT3D (Lorraine et al.) 2023) and Latte3D (Xie et al., 2024) don't open their code and demo, we don't compare with them. As shown in Fig. 7 our semantic alignment is better. Furthermore, benefiting from the training efficiency of 3D GS, quickly fine-tune the generated 3D GS model to enhance the details.
"A glamorous cocktail dress dancing the beach"

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Published in Transactions on Machine Learning Research (03/2026)
4.2.4 Text-to-image-to 3DGS
Besides the direct generalion of lext-to-3DGS, alternative methods also exist. to achieve this goal. For instance, LGM (Tang et al. 2025) and GRM (Xu et al. 2024d) first generate the reference images using a text-to-image diffusion model, followed by the crcation of 3D model from these images. However, since the second phase of inage-to-3DGS trained on the limited 3D dataset, it is hard for them to generale the 3D GS models for complex prompt input. As shown in Fig. [ we demonstrate the comparison result. Furthermore, since these methods require an intermediate image generation step, they are considerably slower than our approach.
4.2.5 Quantitative Comparison
As shown in Tab. 2 we provide the percentage nu- merical comparison of human preference choice. We present participants with five rendered videos along with the corresponding text prompts generated by five baseline models, allowing them select their preferred option for each case. Most of the users ex- pressed a preference for the content generated by our model. Moreover, also render the 10-view images to compute CLIP similarity as the CLIP score.
Method
Instant:3
LucidDreamer
Ours
Preference
37.3%
7.2%
55.4%
CLIP Score
0.322
0.286
0.324
Table Human Preference.
4.3 Discussion about Generalizability
Beyond sentence-level generalizability, our findings indicate that BrightDreamer also correctly in- terpret some unseen words. As demonstrated in Fig. g although the word "banana" is absent from our training dataset, Bright success- fally identifies its color. This capability not only suggests a potential for broad word-level generaliz- ability but also confirms that BrightDroamer effec- tively learns the mapping from text distribution to 3D GS distributions.
"Racing car, banana, lightweight aero kit, sequentiol gearbox"
"Urban micracor, banana, ideal for city life, fuel-efficient"
Figure 9: Demonstration of word-level generalizability The word "banana" never appears in the training set.
4.4 Ablation Studies
As shown in Fig. 10 we validate our network design for training. Compared between Fig, 10 (a) and Fig. 10 (b), our divided triplane generator can reduce the degree chaos the space significantly, which shows the necessity of our division. Notably, we adjust the number of blocks, Le., network depth, to achieve a similar number of parameters. As Fig. 10 (a) and Fig. 10 (c) demonstrate, it necessary pass the coordinate into the Eshape and Fostor. This design can construet gradient pathway toward the TSD Network, ensuring more accurate shape formulation.
5 Conclusion
In this paper, we introduce the first text-driven 3D Gaussians generative framework, BrightDreamer, capable of generating 3D Gaussians within a remarkably low latency 77ms. To efficiently generate millions of 3D Gaussians, we innovatively deform anchor positions and use these new positions centers based on the input prompt. This approach successfully overcomes the challenge of generating large number of positions. Regarding network architecture, we thoroughly reevaluate the triplane generation process and introduce improved alternative strategy. Our key contribution is to greatly euhance the generalized 3D generation, offering novel and efficient way to create 3D assets from text prompts instantly. Extensive experiments demonstrate that Brighe possesses strong semantic understanding and generalization abilities. Due to the limited 3D data, developing methods based on 2D diffusion is an important direction.
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Future work. The spatial resolution of the gen- erated 3D model is relatively low, resulting in lack of fine-grained detail. To address this problem, we will focus on reducing the gpu memory occu- pation of SDS loss achieve higher spatial resolu- tion. Besides, since the LGM (Tang et al. 2025) and GRM (Xu et al., 2024d can generate highly detailed single-object 3D GS models while our method can generate semantic-rich 3D GS models, developing method that combines these two advantages is an interesting research direction. We will also collect broader data to train a larger model. Furthermore, we will also incorporate the better SDS loss design into our framework to achieve higher quality.
w/o Triplane
(c) w/o Coordinate Shortcut
(a) Complete Design
Generator Division
Figure 10: Ablation studies. All models are trained for 10,000 iterations (incomplete training) with the same configuration: (a) Our complete design, (b) Sin- gle generator replacing three separate generators, (c) Coordinate input removed from Fahape and Feotor-
Acknowledgments
This work is supported by the MOE AcRF Tier 1 (Call 2/2025) Grant under Grant No. RG160/25.
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Zhiwen Yan, Weng Fei Low, Yu Chen, and Gim Hee Lee. Multi-scale 3d gaussian splatting for anti-aliased rendering. arXiv preprint arXiv:2811.1008; 2023b.
Xianghui Yang, Huiwen Shi, Bowen Zhang, Fan Yang, Jiacheng Wang, Hongxu Zhao, Xinhai Liu, Xinzhou Wang, Qingxiang Lin, Jiaao Yu, et al. Hunyuan3d-1.0s A unified framework for text-to-3d and image-lo-3d generation. arXiv preprint arXiv:2411 102299, 2024a.
Xiaofeng Yang, Cheng Chen, Xulei Yang, Fayao Liu, and Guosheng Lin. Text-to-image rectified flow plug-and-play priors. arXiv preprint ar Xix:224006. 03293, 2024b.
Zeyu Yang, Hongye Yang, Zijie Pan, Xiatian Zhu, and Li Zhang. Real-time photorealistic dynamic scene representation and rendering with 4d gaussian splatting. arXiv preprint arXin:2310. 10642, 2023a.
Ziyi Yang, Xinyu Gao, Wen Zhou, Shaohui Jiao, Yuqing Zhang, and Xiaogang Jin. Deformable 3d gaussians for high-fidelity monocular dynamic scene reconstruction. arXiv preprint arXiv:2309.191 2023b.
Taoran Yi, Jiemin Fang, Guanjun Wu, Lingxi Xie, Xiaopeng Zhang, Wenyu Liu, Qi Tian, and Xinggang Wang. Gaussiandreamer: Fast generation from text to 3d gaussian splatting with point cloud priors. arXiv preprint XrX:::2300.00 08529, 2023.
Xin Yu, Yuan-Chen Guo, Yangguang Li, Ding Liang, Song-Hai Zhang, and XIAOJUAN QI. Text-to-3d with classifter score distillation. In The Twelfth International Conference on Learning Representations.
Zehao Yu, Anpei Chen, Binbin Huang, Torsten Sattler, and Andreas Geiger. Mip-splatting: Alias-free 3d gaussian splatting. arXie preprint arXiv:2311.16490 2023.
Vladimir Yugay, Yue Li, Theo Gevers, and Martin R Oswald. Gaussian-slam: Photo-realistic dense slam with gaussian splatting. arXiv preprint arXiv:2312.10070 2023.
Xiaoyu Zhou, Xingjian Ran, Yajiao Xiong, Jinlin He, Zhiwei Lin, Yongtao Wang, Deqing Sun, and Ming- Hsuan Yang. Gala3d: Towards text-to-3d complex scene generation via layout-guided generative gaussian splatting. In Forty-fort International Conference on Machine Learning.
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Published in Transactions on Machine Learning Research (03/2026)
Zi-Xin Zou, Zhipeng Yu, Yuan- Guo, Yangguang Li, Ding Liang, Yan-Pei Cao, and Song-Hai Zhang. Triplane meets gaussian splatting: Fast and generalizable single-view 3d reconstruction with transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pottern Recognition, pp. 10324 10335, 2024.
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Published in Transactions on Machine Learning Research (03/2026)
A Training Details of Briame
For clearer illustration of our training pipeline, we provide the details in Algorithm. |
We train our
BrightDreamer for about 2 days on server with 8 GPUs with 80GB memory. Actually, a lower batch size and less training time can also result in similar quality.
Algorithm Training Procedures of BreaheDreamer
Input: S, training prompts set ; B, batch size of prompts; C, batch size of cameras; maz iter, maximum training iterations
for i +- 1 to max iter do
prompts sample B prompts in S;
3D GS Brighl)remme((romppl)):
loss - 0;
for + to B do
comeras + randomly sample C cameras;
images +- render(3n__ae GS cameras);
tezts dir + promptli] added front/side/ back; loss loss SDS(images, texts _dir;; # Eq. 6 end for aptimizer.zero zero zero_grrd()): loss backwad)) pptimizer.step(} end for End.
B Inference Procedures of Our BrightDreamer
In hmg.rithm.
2 we provide the inference details of our BrightDreamer.
C Discussion about Generalization
In Fig. 2 of the main paper, we show the different combinations that don't appear in the training prompts, e.g., 'deep purple' and 'light purple'. Here, we show the word that doesn't appear in the training prompts may also be understood, For example, 'banana' doesn't 't appear in the training process anymore. However, as shown in Fig. and Fig. 27 it can generate the corresponding color accurately.
More Visual Results
From Fig. 12 to Fig. 51, we provide more multi-view results of our BrigheDreamer. Our method shows strong detail control ability.
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Published in Transactions on Machine Learning Research (03/2026)
Algorithm 2 Inference Procedures of Our BrightDreance
Input: the anchor positions; prompt, input text prompt;
Output: 3D_G,, the generated 3D GS;
# Shape Deformation
A TpD(p, prompt);
p' < p p+ 8;
# Triplanc Generation
Tay + TTG (pprompta)
xxz t TTG PTG_XZ(promptar
yyz + TTG Yxpromplas;;
# 3D Gaussian Decoding
Fay + grid [ad_ssmmpeepp [..., [0, 1]], Tzy);
Fuz grid sample(p'(-... (..., [0, 2]], Nzz) );
Fyz + grid sample(p'( [..., [1, 2]], Tyz);
F+ + (Fay + Fay + Fzu)) 3;
8, R, a + FshapelF, p²);
SH + Fodor(FF p/);
# 3D GS Construction
3D 3D_GS + GaussianModel():
3D GS. xyz p';
3D op_cit_ + a:
3D JD_GS__rotation + S:
3D GS. scaling t R;
3D _SG fe__ffatur_d_de de + SH:
return 3D GS;
End.
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Published in Transactions on Machine Learning Research (03/2026)
Figure 11: Electric luxury SUV, apple red, spacious, advanced tech
Figure 12: Electric luxury SUV, yellow, spacious, advanced tech
Figure 13: Electric luxury SUV, forest green, spacious, advanced tech
Figure 14: Electric luxury SUV, cyan, spacious, advancod tech
Figure 15: Electric luxury SUV, deep blue, spacions, advanced tech
Figure 16: Electric luxury SUV, light purple, spacious, advanced tech
Figure 17: Racing car, deep red, wiiht kit, sequential gearbox
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Published in Transactions on Machine Learning Research (03/2026)
Figure 18: Racing car, blaze orange, lightweight aero kit, sequential gearbox
Figure 19: Racing car, banana, lightweight aero kit, sequential gearbox
Figure 20: Racing car, green, lightweight acro kit, sequential gcarbox
Figure 21: Racing car, cyan, lightweight acro kit, soquential gearbox
Figure 22: Family minivan, purple, large capacity, cconomical
Figure 23: Family minivan, yellow, large capacily, economical
Figure 24: Family minivan, apple red, large capacity, economical
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Published in Transactions on Machine Learning Research (03/2026)
Figure 25: Family minivan, orange, large capacity, economical
Figure 26: Urban microcar, orange, ideal for city life, fuel-efficient
Figure 27: Urban microcar, banana, idcal for city life, fuel-cfficient
Figure 28: Electric coupe, forest green, sleck design, autonomous features
Figure 29: Electric coupe, pearl white, sleck design, autonomous features
Figure 30: Vintage convertible, orange, chrome bumpers, white-wall tires
Figure 31: Vintage convertible, apple red, chrome bumpers, white-wall tires
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Published in Transactions on Machine Learning Research (03/2026)
Figure 32: Vintage convertible, yellow, chrome bumpers, white-wall tires
Figure 33: a man wearing backpack is climbing mountain
Figure 34: woman wearing backpack is climbing mountain
Figure 35: an clderly man wearing a backpack climbing mountain
Figure 36: an elderly woman wearing a backpack climbing a mountain
Figure 37: man is trimming his plants
Figure 38: fat man is trimming his plants
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Published in Transactions on Machine Learning Research (03/2026)
Figure 39: a fat and elderly man trimming his plants
Figure 40: an elderly man is trimming his plants
Figure 41: man is playing with dog
Figure 42: a man wearing a backpack is playing with dog
Figure 43: a man is playing with a dog on the lawn
Figure 44: man is playing with dog on the beach
Figure 45: is mowing the lawn
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Published in Transactions on Machine Learning Research (03/2026)
Figure 46: a man wearing a hat is mowing the lawn
Figure 47: woman is mowing the lawn
Figure 48: a woman in long dress is mowing the lawn
Figure 49: A glamorous woman in a cocktail dress is dancing in the park
Figure 50: A glamorous woman in cocktail dress is dancing the beach
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Reviews:
[{"review_id": "OfwmVbaaei", "reviewer": "Reviewer_SEjh", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper proposes BrightDreamer, a feed-forward text-to-3D generative framework that outputs a 3D Gaussian Splatting (3DGS) representation directly from a text prompt in approximately 77 ms. The key idea is to avoid directly generating millions of Gaussian parameters by (i) deforming a fixed 3D anchor grid via a Text-guided Shape Deformation (TSD) network to obtain Gaussian centers, and (ii) generating word-aware triplanes via a Text-guided Triplane Generator (TTG) and decoding per-center features into the remaining Gaussian attributes (scale, rotation, opacity, SH). Training uses SDS with a 2D diffusion model, and the resulting 3DGS can be rendered at purported 700+ FPS.", "claims_and_evidence_yn": "Yes", "claims_explanation": "- Conceptually clean decomposition: predicting Gaussian centers by deforming an anchor lattice and predicting the remaining attributes from triplanes is an intuitive way to sidestep directly regressing large Gaussian sets.\n- The use of token-level cross-attention in both the TSD and TTG to inject fine-grained text guidance into geometry and appearance is reasonable and consistent with recent advances in text-conditioned 3D generation.\n- The argument to avoid channel-splitting for triplanes (three separate generators) targets a concrete representational deficiency and may improve cross-plane coherence in practice.", "audience_yn": "No", "audience_explanation": "**Weaknesses**\n - Comparisons omit several close contemporaries in amortized text-to-3D and triplane-based generators (e.g., SeMv-3D, TPA3D)\n- Key implementation details are missing: number of Gaussians used at inference (effective active splats), rendering resolution for FPS, training steps and wall-clock time, and Gaussian SH order.\n- Several hyperparameters are specified without context (e.g., scale range (-9, -3)) and lack justification or ablation.", "requested_changes": "**Questions for Authors**\n\n- How many Gaussians are active at inference (on average) and what pruning strategy do you use for low-opacity splats? How does pruning affect quality and rendering FPS?\n- What rendering resolution and SH order are used for the reported 705 FPS? Please report FPS as a function of resolution and Gaussian count to aid fair comparison.\n- How many training steps and wall-clock time are required to train the 500M-parameter model? What GPUs are used, and what is the total compute budget?\n- How large is the prompt dataset (number of prompts, categories, source), and what is the distribution of compositional vs single-object prompts? How are train/test splits defined?\n- Can you report standardized quantitative metrics (e.g., CLIP scores on multi-view renders, T3Bench, multi-view consistency metrics) against ATT3D/SeMv-3D/TPA3D/Instant3D and per-prompt baselines?\n- Please provide evidence for the “spatial inhomogeneity” claim: can you quantify cross-plane correspondence/coherence or show consistency metrics improved by your three-generator design?\n- How sensitive are results to the displacement bound β and the anchor grid resolution (e.g., 32^3 vs 64^3 vs 128^3)? Does increasing β improve expressivity or destabilize training?\n- Do you employ any regularization on opacity or sparsity losses to avoid retaining many near-zero-opacity Gaussians? If so, please detail the loss and its weight.\n- What diffusion backbone and guidance scales are used in SDS during training? Did you try multi-view diffusion or image-conditioned priors (e.g., MVDream/Zero123) to improve consistency?\n- How do you ensure that the word-level cross-attention in TTG/TSD scales to complex prompts (e.g., long sentences) without losing local attributes? Any failure cases or mitigation strategies?", "broader_impact_concerns": "The paper presents a technical framework for accelerating text-to-3D generation. I do not see any specific ethical concerns or negative societal impacts arising from this work.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "mcDgx2LlG8", "reviewer": "Reviewer_6PBk", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "### **Summary**\n\nThis paper explores a training-based, feed-forward alternative to existing optimization-driven text-to-3D approaches (e.g., SDS, VSD). Instead of optimizing a new model for every prompt, the proposed method directly generates 3D Gaussian splats from text, enabling both fast inference and applicability to unseen prompts. Qualitative comparisons against four representative types of text-to-3D baselines suggest improvements in both speed and output quality.\n\n### **Strengths**\n\n* The paper is clearly written and easy to follow. Each component is well-motivated, and notations and architectural choices are presented in a clear and structured way.\n* The comparisons with related work are comprehensive and span different classes of text-to-3D methods, which makes the empirical claims more convincing.\n\n### **Weaknesses**\n\n* Although many results look visually appealing, several outputs appear biased toward simplified, cartoon-like styles. This seems related to an \"over-optimization\" issue where finer textures and colors collapse into clean, minimalistic shapes. While this improves text-3D alignment, it may restrict the method’s usefulness when users expect richer details.\n* The comparisons cover mainly \"car\" and \"human\"-related prompts. Evaluating a broader, more diverse set of object categories would help validate generalizability. For instance, baselines such as LucidDreamer can handle avatars, portraits, and other domains; showing results across these types would strengthen the claim of broad applicability, especially given the qualitative nature of text-to-3D evaluation.", "claims_and_evidence_yn": "No", "claims_explanation": "The paper claims improvements in both speed and quality. The evidence for the speed claim is clear and convincing, as demonstrated in Table 1, where the reported latency improvements are substantial. However, the evidence for the quality and generalizability claims is less convincing. Although Figures 6 to 8 show competitive or better results compared to baselines, many outputs appear over-optimized toward simplified, cartoon-like styles. In addition, the qualitative evaluations focus largely on cars and humans, without demonstrating broader category diversity. Without more varied and detailed examples, the claim of strong generalizability remains insufficiently supported.", "audience_yn": "Yes", "audience_explanation": "Text-to-3D generation is an active and rapidly evolving area of interest for both researchers and digital content creators. This paper introduces a feed-forward, optimization-free pipeline, which represents a meaningful shift from the dominant per-prompt optimization paradigm. Given the substantial reduction in generation time and the practical implications for interactive or large-scale applications, the findings are likely to attract attention from the TMLR community.", "requested_changes": "### **Paper Content** (Critical)\n* The paper should address the observed over-optimization issue more explicitly. In particular, it would help if the authors could analyze the underlying causes (e.g., training dynamics, loss formulation, dataset bias) and describe potential solutions or mitigations. Updated results reflecting such improvements would further strengthen the contribution.\n* The qualitative evaluation would benefit from broader coverage across diverse object categories. Including comparisons beyond cars and humans would make it easier for readers to assess generalizability and better support the claims made in the paper.\n\n### **Paper Format** (Minor)\n* Some paragraphs following equations appear to have unnecessary indentation (e.g., after Equations 1 and 6). Revising these would improve readability.\n* It is recommended to update the citation information to reflect the latest accepted venues. For instance, Instant3D is now accepted to ICLR 2024, and LucidDreamer is accepted to CVPR 2024. This helps readers understand the maturity of the referenced work and properly contextualizes the baselines.", "broader_impact_concerns": "It would be helpful for the authors to include a brief statement confirming that the training data does not contain sensitive or personally identifiable information. It may also be worthwhile to clarify that the goal of the work is to advance research in efficient text-to-3D generation, and that the model is not intended for malicious or harmful applications.", "additional_comments": "no", "extra_scores": {}, "extra_text": {}}, {"review_id": "2JGsfujXKr", "reviewer": "Reviewer_CAG7", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper proposes \"BrightDreamer,\" a feed-forward generative framework designed to synthesize 3D Gaussian Splatting (3DGS) representations directly from text prompts. Unlike optimization-based methods (e.g., DreamFusion, DreamGaussian) that require time-consuming per-prompt optimization, BrightDreamer aims to generate 3D assets in approximately 77ms after a one-time training phase.\nThe methodology consists of two primary components:\nText-guided Shape Deformation (TSD): A network that deforms a set of fixed anchor positions (a 3D grid) into the centers of 3D Gaussians based on the input text embedding.\nText-guided Triplane Generator (TTG): A modified triplane generator that addresses spatial inhomogeneity by using separate generators for each plane (XY, XZ, YZ) and integrates text embeddings via cross-attention.\nThe resulting triplanes are decoded into Gaussian attributes (opacity, scaling, rotation, SH). The model is trained using Score Distillation Sampling (SDS) and a Perp-Neg loss. The authors demonstrate that their method is significantly faster than per-prompt optimization methods and shows better semantic alignment for complex prompts than standard optimization baselines.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The paper substantiates its efficiency claims with benchmarks showing sub-100ms generation times and high rendering frame rates (Table 1). Qualitative comparisons against state-of-the-art baselines convincingly demonstrate the model's superior ability to interpret complex, compositional text prompts without significant artifacts. Additionally, the ablation studies in Section 4.4 provide clear empirical support for the effectiveness of the proposed architectural contributions, such as the split triplane generator.", "audience_yn": "Yes", "audience_explanation": "The intersection of text-to-3D generation and 3D Gaussian Splatting is currently a highly active area of research, making this work relevant to a broad section of the ML community. Researchers will be particularly interested in the proposed feed-forward architecture, which effectively addresses the significant latency bottlenecks found in prevailing optimization-based methods. Furthermore, the insights regarding direct parameter prediction for unstructured 3D representations offer valuable contributions to the field of generative modeling.", "requested_changes": "1. The evaluation is deficient in standard quantitative metrics, such as CLIP-Score or FID, and lacks testing on a large-scale held-out set. Relying solely on subjective user studies prevents an objective comparison of generation fidelity and text alignment against baselines.\n\n2. The fixed 256x256 triplane resolution acts as a significant bottleneck, severely constraining the geometric complexity and texture sharpness of the outputs. This limitation prevents the framework from scaling effectively to generate high-fidelity 3D assets.\n\n3. The assertion that using separate generators for each axis resolves \"spatial inhomogeneity\" lacks theoretical justification or variable isolation. The observed improvements in the ablation study may simply result from the increased parameter count rather than the proposed architectural division.\n\n4. The training dataset appears limited to specific closed-set categories like vehicles, which severely restricts open-vocabulary generalization. Consequently, the model likely overfits to this narrow distribution and will struggle to generate concepts outside these specific domains.\n\n5. The generated samples exhibit significant mode collapse, with objects within the same category sharing nearly identical geometries despite varying prompts. The model appears to retrieve a mean shape rather than learning a diverse generative distribution.\n\n6. The claim that coarse outputs serve as effective initializations is contradicted by the visual results, which show negligible geometric improvement after refinement. Starting from such low-fidelity geometry traps the optimization in local minima, preventing the recovery of sharp structural details.", "broader_impact_concerns": "The paper presents a technical framework for accelerating text-to-3D generation. I do not see any specific ethical concerns or negative societal impacts arising from this work.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)