Title: GeoCAD: Local Geometry-Controllable CAD Generation with Large Language Models
Venue: NeurIPS 2025
Accepted: yes
Keywords: CAD generation

large language model
Abstract: Local geometry-controllable computer-aided design (CAD) generation aims to modify local parts of CAD models automatically, enhancing design efficiency. 
It also ensures that the shapes of newly generated local parts follow user-specific geometric instructions (e.g., an isosceles right triangle or a rectangle with one corner cut off).
However, existing methods encounter challenges in achieving this goal.
Specifically, they either lack the ability to follow textual instructions or are unable to focus on the local parts.
To address this limitation, we introduce GeoCAD, a user-friendly and local geometry-controllable CAD generation method. 
Specifically, we first propose a complementary captioning strategy to generate geometric instructions for local parts.
This strategy involves vertex-based and VLLM-based captioning for systematically annotating simple and complex parts, respectively.
In this way, we caption $\sim$221k different local parts in total.
In the training stage, given a CAD model, we randomly mask a local part.
Then, using its geometric instruction and the remaining parts as input, we prompt large language models (LLMs) to predict the masked part.
During inference, users can specify any local part for modification while adhering to a variety of predefined geometric instructions.
Extensive experiments demonstrate the effectiveness of GeoCAD in generation quality, validity and text-to-CAD consistency.

GeoCAD: Local Geometry-Controllable CAD Generation with Large Language Models

Abstract

Local geometry-controllable computer-aided design (CAD) generation aims to modify local parts of CAD models automatically, enhancing design efficiency. It also ensures that the shapes of newly generated local parts follow user-specific geometric instructions ( e.g. , an isosceles right triangle or a rectangle with one corner cut off). However, existing methods encounter challenges in achieving this goal. Specifically, they either lack the ability to follow textual instructions or are unable to focus on the local parts. To address this limitation, we introduce GeoCAD, a user-friendly and local geometry-controllable CAD generation method. Specifically, we first propose a complementary captioning strategy to generate geometric instructions for local parts. This strategy involves vertex-based and VLLM-based captioning for systematically annotating simple and complex parts, respectively. In this way, we caption ∼ 221k different local parts in total. In the training stage, given a CAD model, we randomly mask a local part. Then, using its geometric instruction and the remaining parts as input, we prompt large language models (LLMs) to predict the masked part. During inference, users can specify any local part for modification while adhering to a variety of predefined geometric

instructions. Extensive experiments demonstrate the effectiveness of GeoCAD in generation quality, validity and text-to-CAD consistency. Code will be available at https://github.com/Zhanwei-Z/GeoCAD .

1 Introduction

Computer-Aided Design (CAD) is pivotal in industrial design, driving innovation and efficiency across diverse domains such as mechanical manufacturing [8, 14, 17]. In CAD tools (such as SolidWorks and AutoCAD), the sketch-extrude-modeling (SEM) workflow [35, 45, 50, 48] is commonly employed, enabling users to control the parametric design process effectively. During this process, users sequentially extrude each 2D sketch into 3D shapes to construct complex solid CAD models, with each sketch comprising one or multiple local loops. Each local loop typically represents a pattern or geometric shape, serving as the fundamental closed-path element of a CAD model [50, 48].

In practice, any minor mistake in local parts ( i.e. , local loops 3 ) of a CAD model can potentially result in significant systemic errors. Thus, after drawing a draft CAD model, users generally need to modify its local parts to ensure that the final CAD product meets the expected functional or aesthetic requirements. Compared to manual modifications, if a deep-learning method can automatically adjust the shapes of local parts according to user-defined geometric instructions 4 ( e.g. , an isosceles right triangle or a rectangular shape with one corner removed), it would significantly reduce labor costs in CAD product optimization. Moreover, such a method must retain the remaining CAD parts unchanged while ensuring that the newly generated local parts integrate with them without conflict. We refer to these capabilities as local geometry-controllable CAD generation .

Unfortunately, existing controllable CAD generation methods face challenges in achieving local geometry-control. Specifically, [50, 48, 46, 23, 57] typically take partial CAD parts or attributes ( e.g. , incomplete sketches, topological or geometric parameters) as input and automatically generate new CAD models. Yet, they lack the ability to follow textual instructions, which hinders users from expressing their requirements intuitively and conveniently. To resolve this limitation, some text-toCAD methods based on LLMs or transformers [41] have demonstrated meaningful progress [24, 18, 47, 55, 44, 43, 2, 61]. However, these methods are not applicable for local geometry-controllable generation. Specifically, [24, 18, 47, 44, 43, 2] typically generate a new CAD model from scratch based on textual instructions, making it difficult to fully focus on the required local parts. In addition, [18, 55, 43, 44] primarily collect textual descriptions of CAD models from global 3D views rather than local 2D views. These 3D views are generally oblique, which prevents capturing accurate geometric attributes (such as length and angle) of local parts for training. [61] can focus on local parts well but incorporates little geometric constraint, thereby struggling to follow geometric instructions.

In this paper, we propose GeoCAD, a user-friendly and local geometry-controllable CAD generation method. As shown in Fig. 1, GeoCAD takes the original CAD model, the local parts (highlighted in blue), and user-specific geometric instructions as inputs. The local parts are then generated by GeoCAD to align with the instructions, and are combined with the remaining parts to create new CAD models. To achieve this objective, the primary challenge is addressing the insufficiency of training data, specifically the geometric instructions for local parts. Given that manual captioning is prohibitively costly and labor-intensive, we introduce a complementary captioning strategy. Specifically, we categorize local parts into simple and complex groups based on their internal side types and numbers. Simple parts correspond to common geometric shapes ( e.g. , triangles with three lines, quadrilaterals with four lines), while complex parts typically represent more intricate visual patterns. For complex parts, we render them as 2D images and then employ advanced vision large language models (VLLMs) [1, 4] to derive descriptive captions. However, for simple parts, VLLMs fail to achieve accurate fine-grained captioning. For example, VLLMs do not reliably distinguish whether a quadrilateral is a rhombus based solely on an image. To overcome this limitation, we introduce a vertex-based captioning method for simple parts. This involves extracting vertex coordinates from the original CAD model and then analyzing geometric attributes for accurate classification. For instance, if a quadrilateral has four lines of equal length, it is a rhombus; if it contains right angles, it is further categorized as a square. Utilizing the complementary strategy, we have successfully captioned approximately 221k different local parts, comprising 116k complex parts and 105k simple

parts. Inspired by the success of LLMs in text-to-CAD generation [47, 2, 55, 44, 43, 61], during training, given a CAD model, we randomly mask a local part and prompt LLMs to predict this part using the corresponding geometric instruction and the remaining visible parts as inputs. Once trained, in real-world applications, users can mask any local part for modification based on various geometric instructions. The new local parts generated by GeoCAD are then integrated with the remaining parts of the original CAD model to form new CAD models. Overall, our contributions are:

We propose GeoCAD, a local geometry-controllable CAD generation method, enabling users to express design intent for specific parts through geometric instructions.

To the best of our knowledge, GeoCAD is the first to achieve local geometry-control in the CAD generation field. To achieve this, we propose a complementary captioning pipeline to annotate ∼ 221k distinct local parts for the following two-stage LLM fine-tuning.

Extensive experiments demonstrate that GeoCAD significantly enhances generation quality, validity, and text-to-CAD consistency in local geometry-controllable CAD generation.

2 Related Work

CAD Model Generation. Existing CAD generation methods can be categorized into three types: constructive solid geometry (CSG), boundary representation (B-rep) and sketch-and-extrude modeling (SEM). CSG constructs CAD models by combining primitives ( e.g. , cubes or spheres) into a tree [21, 6, 53, 34]. B-rep denotes CAD models as interconnected faces, edges, and vertices [3, 7, 49, 37]. Compared to CSG and B-rep, SEM-based methods [45, 50, 48, 17, 31, 55, 43, 44, 23, 61, 57, 5] are consistent with prevailing CAD tools, allowing users to sequentially extrude sketches into 3D shapes, with each sketch comprising one or multiple loops. Notably, within a sketch, any loop nested inside another loop serves as a hole. Recently, SEM-based controllable CAD generation has garnered a lot of attention due to its potential to revolutionize the design process [50, 48, 46, 23, 57]. Specifically, these methods allow for some level of control over the parts or attributes of the original CAD models. Among them, [50, 46, 23, 57] achieve sketch-level control, while [48] offers finer-grained control over local loops. Despite these capabilities, these methods struggle to follow textual instructions, limiting users from conveying their design intent in an intuitive and convenient manner.

On the other hand, current text-to-CAD methods that have demonstrated meaningful progress [24, 18, 47, 55, 44, 43, 2]. Notably, [24, 18, 47, 44, 43, 2] typically generate a new CAD model from the ground up based on textual instructions, which limits their ability to precisely target or refine specific local parts as per user specifications. Moreover, [18, 55, 43] primarily gather textual descriptions from global 3D perspectives rather than localized 2D views. These 3D perspectives are typically captured in oblique orientations, which limits their ability to precisely quantify critical geometric attributes ( e.g. , length and angle) of local parts during the training process. [61] can effectively concentrate on the generation of local parts but fails to follow geometric instructions. To sum up, current studies lack the ability to achieve local geometry-controllable generation.

Large Language Models (LLMs). Compared to traditional deep-learning based models [62, 60, 9, 12, 36, 25], LLMs have recently demonstrated a remarkable ability to follow textual instructions [40, 1, 54, 51, 28, 10, 29]. Leveraging this capability, LLMs have shown notable versatility and efficacy across diverse applications [58, 22, 52, 13, 59, 38]. Users can employ various textual instructions to direct LLMs in accomplishing diverse tasks like code generation [15, 11] and question answering [39, 20]. As a branch, vision large language models (VLLMs) have also achieved significant success in vision domains [27, 56, 26]. More recently, both LLMs and VLLMs have shown promise in CAD generation [47, 2, 55, 44, 43, 61]. Specifically, [47, 55, 43, 44, 61] primarily rely on VLLMs for CAD caption synthesis or fine-tune LLMs or transformers [41] to generate CAD models from scratch. On the other hand, [2] employs a training-free manner to generate CAD codes via informative prompts. As mentioned above, these methods either cannot effectively focus on local generation or struggle to follow geometric instructions accurately. Distinguished from them, our GeoCAD excels in local part generation while precisely adhering to geometric instructions.

3 Methodology

In this section, we present GeoCAD, a user-friendly and local geometry-controllable CAD generation method. As shown in Fig. 1, GeoCAD incorporates three inputs: (1) an original CAD model,

CAD Text

Representation

B

C

D

A(26,31); B (31,18);

C (36,31); D (31,44);

Thus, AB=BC=CD=DA.

a rhombus

E

F

G

a semicircle

a shape that looks

like a letter L

{

}

In image format

Text-to-CAD Rending

VLLM

{

}

In image format

VLLM

an irregular polygon

resembling  a letter V

CAD Text

Representation

E (35,31); F (31,27); G (27,31);

Thus, Ø(arc EFG)=EG,

where Ø denotes the diameter.

Vertex-based Captioning

A

line,2,42

…

<loop_end>

Local Loop

Text

line,30,42

…

<loop_end>

line,26,31

…

<loop_end>

arc,35,31,31,27

…

<loop_end>

Local Loop

Text

VLLM-based Captioning

Simple Loop Examples

(a)

(b)

Vertex Extraction

→

→

represented in a hierarchically textual format proposed by FlexCAD [61], (2) the local part designated for modification, and (3) geometric instructions specified by the user. GeoCAD then generates new CAD models, altering only the designated local part while closely adhering to the provided instructions. To achieve this, we first propose a complementary captioning strategy to generate ∼ 221k geometric instructions for local parts (Sec. 3.1). Building on these instructions, we then formulate a two-stage training pipeline to fine-tune LLMs for local CAD generation (Sec. 3.2).

3.1 Complementary Captioning for Local Parts

The main challenge in achieving local geometry-control is tackling the lack of training data, particularly concerning geometric instructions for local parts within 3D CAD models. Since manual captioning is excessively expensive and labor-intensive, we propose a complementary captioning strategy. In the beginning, we collect local parts ( i.e. , local loops) from the CAD models within the DeepCAD dataset [45], filtering out duplicates and discarding invalid ones ( i.e. , those that are not closed loops or involve intersecting line segments). Subsequently, we adopt the textual format introduced in FlexCAD [61] to represent CAD models and their local parts, where each local part is denoted as a contiguous string comprising the side type and vertex coordinates, as illustrated in Fig. 2. These local parts are then categorized into simple and complex groups based on their internal side numbers and types. Specifically, as shown in the lower part of Fig. 2(a), simple parts represent common geometric shapes ( e.g. , triangles with three lines, quadrilaterals with four lines, sectors with two lines and an arc), making up roughly 50% of the entire set of local parts, while complex parts typically exhibit more intricate visual patterns as shown in the lower part of Fig. 2(b).

As shown in the upper part of Fig. 2(b), for complex parts, we transform them into 2D images and then leverage powerful VLLMs [1, 4] to obtain their geometric instructions (see the detailed prompts to guide VLLMs in the appendix). However, VLLMs exhibit limitations in fine-grained geometric descriptions for simple parts. For instance, they struggle to reliably discern whether a quadrilateral is a rhombus according to an image alone. To address this problem, we propose a vertex-based captioning method for simple parts. As shown in the upper part of Fig. 2(a), we first extract vertex coordinates from the original CAD text representation and then analyze geometric properties to precisely categorize these parts. For instance, given a quadrilateral, we can calculate its side lengths and inter-side angles based on its vertex coordinates. If it has four lines of equal length, it is a rhombus; if it includes right angles, it is further categorized as a square. Moreover, for partial simple parts, we also incorporate key dimensional parameters into the captions (such as the radius length of a circle and the side length of a square). In total, we annotate nearly 221k distinct local parts, consisting of 116k complex parts and 105k simple parts.

3.2 Fine-tuning LLMs with Geometric Instruction

With the geometric instructions derived in Sec. 3.1, we fine-tune LLMs to achieve local geometrycontrollable CAD generation. The training procedure comprises two stages:

Instruction:

Generate a string that denotes a right trapezoid.

Answer (1):

line,31,31 <curve_end> line,31,39 <curve_end> line,39,39 <curve_end> line,47,31 <curve_end> <loop_end>

Answer (2):

line,41,37 <curve_end> line,41,45 <curve_end> line,49,45 <curve_end> line,57,37 <curve_end> <loop_end>

Answer (3):

line,31,31 <curve_end> line,31,43 <curve_end> line,43,43 <curve_end> line,55,31 <curve_end> <loop_end>

Answer (4):

line,31,31 <curve_end> line,39,31 <curve_end> line,39,23 <curve_end> line,31,15 <curve_end> <loop_end>

Answer (5):

line,31,31 <curve_end> line,31,39 <curve_end> line,23,39 <curve_end> line,15,31 <curve_end> <loop_end>

Translation

Rotation

Reflection

Scaling

(1)

(2)

(3)

(4)

(5)

Prompt Design

Instruction:

Below is a partial description of a CAD sequence where one command has been replaced with the

string "[loop mask]":

line,26,31 <curve_end> line,31,18 <curve_end> line,36,31 <curve_end> line,31,44 <curve_end> <loop_end>

<face_end> <sketch_end> add,31,39,31,31,31,1,0,0,0,1,0,0,0,1,37,24,28 <extrusion_end> [loop mask]

<face_end> <sketch_end> cut,31,39,31,31,31,1,0,0,0,1,0,0,0,1,37,24,28 <extrusion_end>

Generate a string that could replace "[loop mask]" in the CAD sequence. Notably, the string denotes a semicircle.

Answer:

arc,35,31,31,27 <curve_end> line,27,31 <curve_end> <loop_end>

Prompt Design

B

Stage 1: Pre-training for CAD-text Alignment (Optional). As mentioned in Sec. 3.1, we follow [61] to represent local parts using their internal side types and vertex coordinates. Since such CAD-specific geometric representation is typically absent from the pretraining corpus of LLMs, this stage focuses on aligning the representation of local parts with textual geometric instructions, thereby further enhancing the LLMs' understanding of the CAD-specific representation. Specifically, as illustrated in Fig. 3, for each local part, we apply random data augmentation via translation, scaling, rotation, and reflection. Notably, the geometric instructions of augmented samples remain unchanged due to the geometric consistency ( e.g. , the geometric instructions of the augmented samples in Fig. 3 are all right trapezoids). Subsequently, for the initial and augmented samples, their corresponding instructions and answers are all employed to fine-tune LLMs.

Stage 2: Instruction Fine-Tuning for Local Geometry-Control. In practice, when modifying a specific part of a CAD model, it is crucial to retain the other parts of the CAD model unchanged. Additionally, the newly generated part should integrate with them without any conflicts. To this end, inspired by FlexCAD [61], at each epoch, for a given CAD model, we randomly mask a local part and design geometric instructions. These instructions are employed to prompt LLMs to predict this masked part autoregressively. However, FlexCAD's training process has one critical limitation: its prompts lack geometric constraints during training. Consequently, once trained, FlexCAD struggles to follow geometric instructions. In light of this, as shown in Fig. 4, our prompts incorporate the geometric instructions as constraints when fine-tuning LLMs to generate predictions. As shown in Fig. 5, during stages 1 and 2, the cross-entropy (CE) loss between the predicted tokens and the answer

Fine-tuned LLM

(a) The Training Process

Pretrained LLM

LoRA

+

Infilling&Rendering

...

...

Prompt Design

Instruction Tokens

Answer Tokens

CE Loss

Instruction+Answer

{GI}=

...

Training set

(In text format)

...

Training set

(In text format)

...

Masking

...

Masking

...

Predicted Tokens

...

...

Predicted Tokens

...

...

Test set

(In text format)

...

User-defined

Loop Masking

Prompt Design

Instruction

Below is a partial .... Notably,

the string denotes {GI}.

Instruction

Below is a partial .... Notably,

the string denotes {GI}.

a square

a rounded rectangle

an annular sector

...

Predicted Tokens

(b) The Inference Process

a square

a rounded rectangle

an annular sector

...

tokens is back-propagated to update the trainable parameters of LLMs. Furthermore, we follow FelxCAD [61] to fine-tune the LLM using LoRA [16], which enables partial parameters training while freezing most parameter weights. This strategy allows us to retain the advantages of large-scale pre-trained models while accelerating convergence during optimization.

Inference. In practical applications, users can selectively mask any local part for modification, guided by various geometric instructions ( e.g. , a square, a rounded rectangle, or an annular sector). The mask part is then replaced with the predicted local parts, which are seamlessly integrated with the remaining parts of the original CAD model to form new CAD models, as shown in Fig. 5(b).

4 Experiments

4.1 Experimental Setup

Datasets. To maintain consistency with prior research [61], we evaluate our GeoCAD on DeepCAD [45], a large-scale 3D sketch-extrude-modeling CAD dataset. This dataset contains 178,238 sketchextrusion sequences, which are randomly partitioned into training, validation, and test subsets at a 90%-5%-5% ratio. Following established preprocessing protocols from SkexGen [50], we first eliminate duplicate and invalid sequences to ensure data quality. Subsequently, we follow FlexCAD [61] to convert the remaining CAD sequences into concise textual representations, which can be easily fed into LLMs. Within this dataset, we systematically collect and caption approximately 221k distinct local parts, including 116k complex parts and 105k simple parts.

Implementation Details. To ensure a fair comparison with FlexCAD [61], we adopt Llama-3-8B [32] as the base LLM, which achieves competitive performance among open-source LLMs. We use the same LoRA [16] setting as used in [61], with a rank of 8 and an alpha of 32. In stage 1, we implement translation, scaling, rotation, and reflection for simple parts, while applying only translation and scaling to complex parts to avoid semantic inconsistencies in captions. The model is trained on 8 A100 GPUs using AdamW [30], with a batch size of 32, a cosine annealing learning rate initialized at 5 × 10 -4 , and trained for 10 and 30 epochs across stage 1 and stage 2. During inference, we set the temperature τ and Top -p at 0.9 and 0.9 to balance quality and validity in local generation.

Metrics. As this work pioneers local geometry-controllable CAD generation, we propose a comprehensive evaluation benchmark based on three key aspects:

1) Generation quality. We adopt metrics from prior work [50, 48, 61]. Specifically, Coverage (COV) measures the diversity of generated shapes and helps identify whether the model suffers from mode collapse. Minimum Matching Distance (MMD) reports the average minimum distance between real data and the generated set. Jensen-Shannon Divergence (JSD) quantifies the similarity between the distributions of real and generated samples. Together, these metrics measure generation quality on generated CAD models with respect to the test set.

2) Validity. Predicted local parts must form closed loops and must not contain intersecting line segments. In addition, these parts should seamlessly integrate with the existing parts to enable successful rendering into valid 3D shapes, rather than invalid or empty outputs. Following [61], we use Prediction Validity (PV) to quantify the overall validity rate of the generated predictions.

3) Text-to-CAD consistency. The generated 2D local parts should be consistent with user-defined geometric instructions. To measure this, we propose a vertex-based score (Ver-score) for assessing simple parts, and a VLLM-based score (VLLM-score) to evaluate complex parts. Finally, Realism denotes the human evaluation score, manually assessing whether the generated 3D CAD models fully satisfy user requirements for local geometry-control. See details of these metrics in the appendix.

4.2 Performance Comparision with Existing Methods

Baselines. As discussed above, most controllable CAD generation methods are not applicable to the local geometry-control task. Thus, we compare our GeoCAD with OpenAI-o3 [33], one of the most powerful closed-source LLMs, and FlexCAD [61], a state-of-the-art baseline for local CAD generation by fine-tuning LLMs. Without fine-tuning, the output format of the vanilla OpenAI-o3 model does not conform to the textual representation defined in [61], making it unable to directly generate local parts. To address this, we improve the performance of OpenAI-o3 with a few-shot learning strategy. Moreover, we manually enhance FlexCAD's performance when generating simple

a quarter-circle

a semicircle

a shape resembling an

elongated oval

an isosceles trapezoid

a right trapezoid

a parallelogram

an isosceles triangle

a right triangle

an isosceles right triangle

a rectangle with

one corner cut off

a rectangle with a smaller

rectangle cut off

an annular sector

a rectangular shape with

rounded corners

an elongated rectangle

with rounded ends

a regular polygon

with 6 sides

a letter 'T'

a letter 'H'

a letter 'L'

OpenAI-o3.FlexCAD

Ours

OpenAI-o3

ElexCAD

Ours

OnenAL-o3

ElexCAD

Ours

parts by adjusting the internal curve types and numbers. For example, when aiming to generate an isosceles trapezoid, we try our best to guide FlexCAD to produce a loop composed of four lines.

Quantitative Results. We randomly sampled 1k CAD models from the test set. For each CAD model, a local part was randomly masked, and each method was prompted to generate 10 new parts using 5 simple and 5 complex geometric instructions. Here, simple and complex instructions correspond to the generation of simple and complex local parts, respectively. After infilling, this process yielded a total of 10k generated CAD models per method. To compute the COV, MMD, and JSD metrics, which rely on a subset of ground-truth samples, we randomly selected 3k CAD models from the test set and calculated the average results over three separate runs. As presented in Table 1, OpenAI-o3 delivers subpar performance without fine-tuning, even when supported by five-shot

an rectangle with

rounded ends

an elongated

oval shape with

pointed ends

a hexagon with

sides of varying

lengths and angles

a regular polygon

with 8 sides.

an elongated

rectangle with

one rounded end

a regular polygon

with 6 sides.

a major-arc loop

a minor-arc loop

an isosceles

trapezoid

an irregular

quadrilateral

a square

a rectangle

an obtuse

triangle

a right trapezoid

a circle

a semicircle

a rectangle

an isosceles

right triangle

a right trapezoid

a semicircle

a letter 'E'

a letter 'Y'

a letter 'A'

a letter 'T'

a letter 'X'

a letter 'H'

a letter 'V'

a letter 'Y'

a letter 'T'

a letter 'I'

a letter 'L'

a letter 'V'

a letter 'H'

a letter 'Z'

an isosceles

right triangle

a circle with the radius length of

{R}

units

X = 50, Y=30

R = 20

R = 15

R = 10

R = 5

a square with the side length of

{X}

units

a rectangle with the length of

{X}

units and the width of

{Y}

units

X = 30

X = 25

X = 20

X = 15

X = 40, Y=30

X = 20, Y=30

X = 10, Y=30

X = 10, Y=25

X = 10, Y=35

X = 10, Y=45

X = 10, Y=55

X

Y

learning. In comparison, our proposed GeoCAD achieves superior results over the state-of-the-art baseline, FlexCAD, particularly in terms of Ver-score, VLLM-score, and Realism, with significant improvements of up to 38.7%, 41.4%, and 23.4%, respectively. This is mainly because FlexCAD lacks the ability to align with geometric instructions during the generation of local parts. On the other hand, the few-shot learning ability of LLMs leads to performance improvements for both FlexCAD and our GeoCAD. Overall, the results demonstrate the clear advantage of our GeoCAD in generation quality, validity, and text-to-CAD consistency.

Qualitative Results. To intuitively demonstrate performance, we randomly selected six CAD models from the test set. As shown in Fig. 6, the results clearly highlight that our GeoCAD significantly improves controllability and text-to-CAD consistency compared to existing baseline methods. In particular, GeoCAD is able to modify local parts in a way that closely adheres to user-defined geometric instructions. In contrast, FlexCAD struggles to comply with such instructions and frequently generates overly common shapes, such as circles or rectangles (see green boxes in Fig. 6). Moreover, as shown in the red boxes in Fig. 6, both OpenAI-o3 and FlexCAD often produce local parts that fail to align properly with the remaining parts of the original CAD models, resulting in outputs that are functionally or aesthetically implausible. These visualizations further validate the superior local controllability and effectiveness of our proposed GeoCAD.

Furthermore, we provide additional qualitative results generated by GeoCAD. As illustrated in Fig. 7, given a CAD model, GeoCAD is capable of effectively modifying any target loop within it to form simple or complex patterns. Moreover, for certain simple parts, we incorporate specific dimensional constraints into the instructions, such as the radius of a circle, the side length of a square, and the

a narrow, rounded-

end rectangle

a  trapezoid-like

loop

a trapezoid

a parallelogram

a slender

rectangle with

rounded ends

a linear

rectangle with

smoothed ends

a rectangle

missing one of

its corners

a rectangle with

one corner cut off

a rectangular

shape with one

corner removed

a right triangle

a major-arc loop

a parallelogram-

shaped loop

a parallelogram-

like shape

a right-angled

triangle

a triangle with

a right angle

a major-arc-

shaped loop

a loop formed by

a major arc

a shape resembling a

trapezoid

length and width of a rectangle. As shown in Fig. 8, GeoCAD not only accurately generates the desired shapes but also adheres closely to the specified dimensional parameters. On the other hand, as shown in Fig. 9, GeoCAD demonstrates robust generalization capabilities in accurately understanding and executing semantically similar instructions, even when some of these instructions ( e.g. , a narrow, rounded-end rectangle and a right-angled triangle) never appeared in the training data.

4.3 Ablation Studies

We conduct a series of ablation studies under the same experimental settings described in Table 2.

Effectiveness of the complementary captioning strategy. As shown in Table 2, using only vertexbased or VLLM-based captioning fails to generate complex parts ( e.g. , a letter V) or simple parts ( e.g. , a trapezoid), thereby failing to obtain the corresponding Ver-score and VLLM-score. In contrast, the complementary captioning integrating both of them leads to improved performance.

Effectiveness of Pre-training. As depicted in Table 2, omitting stage 1 results in the poorest performance, demonstrating that pre-training is essential for achieving preliminary text-CAD alignment. Additionally, excluding data augmentation during pre-training leads to a performance decline, indicating that diverse augmented samples enhance GeoCAD 's alignment capability. Together, these findings confirm the effectiveness of the pre-training process.

5 Conclusion

In this paper, we introduce a local geometry-controllable CAD generation method, GeoCAD, enabling users to specify design intent for specific parts through geometric instructions. To the best of our

knowledge, GeoCAD is the first to achieve local geometry-control in the CAD generation field. To accomplish this, GeoCAD introduces both vertex-based and VLLM-based captioning pipelines and employs a two-stage training strategy for LLM fine-tuning. Extensive qualitative and quantitative evaluations demonstrate that GeoCAD substantially improves generation quality, validity, and text-toCAD consistency in local geometry-controllable CAD generation.

References

J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 , 2023.

K. Alrashedy, P. Tambwekar, Z. H. Zaidi, M. Langwasser, W. Xu, and M. Gombolay. Generating CAD code with vision-language models for 3d designs. In The Thirteenth International Conference on Learning Representations , 2025.

S. Ansaldi, L. De Floriani, and B. Falcidieno. Geometric modeling of solid objects by using a face adjacency graph representation. ACM SIGGRAPH Computer Graphics , 19(3):131-139, 1985.

J. Bai, S. Bai, S. Yang, S. Wang, S. Tan, P. Wang, J. Lin, C. Zhou, and J. Zhou. Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond. arXiv preprint arXiv:2308.12966 , 2023.

C. Chen, J. Wei, T. Chen, C. Zhang, X. Yang, S. Zhang, B. Yang, C.-S. Foo, G. Lin, Q. Huang, et al. Cadcrafter: Generating computer-aided design models from unconstrained images. arXiv preprint arXiv:2504.04753 , 2025.

J. Chen, Z. Shen, M. Zhao, X. Jia, D.-M. Yan, and W. Wang. Fr-csg: Fast and reliable modeling for constructive solid geometry. IEEE Transactions on Visualization and Computer Graphics , 2024.

K. Cherenkova, E. Dupont, A. Kacem, G. Gusev, and D. Aouada. Spelsnet: Surface primitive elements segmentation by b-rep graph structure supervision. Advances in Neural Information Processing Systems , 37:1251-1269, 2024.

J. G. Cherng, X.-Y. Shao, Y. Chen, and P. R. Sferro. Feature-based part modeling and process planning for rapid response manufacturing. Computers & industrial engineering , 34(2):515-530, 1998.

Y. Dai, W. Zhu, R. Li, Z. Ren, X. Zhou, X. Li, J. Li, and J. Yang. Harmonious group choreography with trajectory-controllable diffusion. arXiv preprint arXiv:2403.06189 , 2024.

B. C. Das, M. H. Amini, and Y. Wu. Security and privacy challenges of large language models: A survey. ACM Computing Surveys , 57(6):1-39, 2025.

Y. Dong, J. Ding, X. Jiang, G. Li, Z. Li, and Z. Jin. Codescore: Evaluating code generation by learning code execution. ACM Transactions on Software Engineering and Methodology , 34(3):1-22, 2025.

Y. Dong, Y. Li, Z. Huang, W. Bian, J. Liu, H. Bao, Z. Cui, H. Li, and G. Zhang. A global depth-range-free multi-view stereo transformer network with pose embedding. arXiv preprint arXiv:2411.01893 , 2024.

S. Fan, L. Xie, C. Shen, G. Teng, X. Yuan, X. Zhang, C. Huang, W. Wang, X. He, and J. Ye. Improving complex reasoning with dynamic prompt corruption: A soft prompt optimization approach. In The Thirteenth International Conference on Learning Representations , 2025.

Y. Ganin, S. Bartunov, Y. Li, E. Keller, and S. Saliceti. Computer-aided design as language. Advances in Neural Information Processing Systems , 34:5885-5897, 2021.

X. Gu, M. Chen, Y. Lin, Y. Hu, H. Zhang, C. Wan, Z. Wei, Y. Xu, and J. Wang. On the effectiveness of large language models in domain-specific code generation. ACM Transactions on Software Engineering and Methodology , 34(3):1-22, 2025.

E. J. Hu, yelong shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen. LoRA: Low-rank adaptation of large language models. In International Conference on Learning Representations , 2022.

M. S. Khan, E. Dupont, S. A. Ali, K. Cherenkova, A. Kacem, and D. Aouada. Cad-signet: Cad language inference from point clouds using layer-wise sketch instance guided attention. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 4713-4722, 2024.

M. S. Khan, S. Sinha, T. Uddin, D. Stricker, S. A. Ali, and M. Z. Afzal. Text2cad: Generating sequential cad designs from beginner-to-expert level text prompts. Advances in Neural Information Processing Systems , 37:7552-7579, 2024.

S. Koch, A. Matveev, Z. Jiang, F. Williams, A. Artemov, E. Burnaev, M. Alexa, D. Zorin, and D. Panozzo. Abc: A big cad model dataset for geometric deep learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 9601-9611, 2019.

J. Kuang, Y. Shen, J. Xie, H. Luo, Z. Xu, R. Li, Y. Li, X. Cheng, X. Lin, and Y. Han. Natural language understanding and inference with mllm in visual question answering: A survey. ACM Computing Surveys , 57(8):1-36, 2025.

D. H. Laidlaw, W. B. Trumbore, and J. F. Hughes. Constructive solid geometry for polyhedral objects. In Proceedings of the 13th annual conference on Computer graphics and interactive techniques , pages 161-170, 1986.

J. Li, K. Pan, Z. Ge, M. Gao, W. Ji, W. Zhang, T.-S. Chua, S. Tang, H. Zhang, and Y. Zhuang. Fine-tuning multimodal LLMs to follow zero-shot demonstrative instructions. In The Twelfth International Conference on Learning Representations , 2024.

P. Li, W. Zhang, J. Guo, J. Chen, and D.-M. Yan. Revisiting cad model generation by learning raster sketch. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 39, pages 4869-4877, 2025.

X. Li, Y. Song, Y. Lou, and X. Zhou. Cad translator: An effective drive for text to 3d parametric computer-aided design generative modeling. In Proceedings of the 32nd ACM International Conference on Multimedia , pages 8461-8470, 2024.

X. Li, Z. Wang, Y. Zou, Z. Chen, J. Ma, Z. Jiang, L. Ma, and J. Liu. Difiisr: A diffusion model with gradient guidance for infrared image super-resolution. In Proceedings of the Computer Vision and Pattern Recognition Conference , pages 7534-7544, 2025.

Y. Li, S. Jiang, B. Hu, L. Wang, W. Zhong, W. Luo, L. Ma, and M. Zhang. Uni-moe: Scaling unified multimodal llms with mixture of experts. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2025.

H. Liu, C. Li, Q. Wu, and Y. J. Lee. Visual instruction tuning. Advances in neural information processing systems , 36, 2024.

Y. Liu, J. Wu, Y. He, H. Gao, H. Chen, B. Bi, J. Zhang, Z. Huang, and B. Hooi. Efficient inference for large reasoning models: A survey. arXiv preprint arXiv:2503.23077 , 2025.

Y. Liu, S. Zhai, M. Du, Y. Chen, T. Cao, H. Gao, C. Wang, X. Li, K. Wang, J. Fang, J. Zhang, and B. Hooi. Guardreasoner-vl: Safeguarding vlms via reinforced reasoning. arXiv preprint arXiv:2505.11049 , 2025.

I. Loshchilov and F. Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations , 2018.

W. Ma, S. Chen, Y. Lou, X. Li, and X. Zhou. Draw step by step: Reconstructing cad construction sequences from point clouds via multimodal diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 27154-27163, 2024.

A. Meta. Introducing meta llama 3: The most capable openly available llm to date. Meta AI , 2024.

OpenAI. Openai o3 and o4-mini system card. 2025.

P. K. Romano, P. A. Myers, S. R. Johnson, A. Kolsek, and P. C. Shriwise. Point containment algorithms for constructive solid geometry with unbounded primitives. Computer-Aided Design , 178:103803, 2025.

T. M. Shahin. Feature-based design-an overview. Computer-Aided Design and Applications , 5(5):639-653, 2008.

F. Shen and J. Tang. Imagpose: A unified conditional framework for pose-guided person generation. Advances in neural information processing systems , 37:6246-6266, 2024.

Z. Shen, M. Zhao, D.-M. Yan, and W. Wang. Mesh2brep: B-rep reconstruction via robust primitive fitting and intersection-aware constraints. IEEE Transactions on Visualization and Computer Graphics , 2025.

P. Shojaee, K. Meidani, S. Gupta, A. B. Farimani, and C. K. Reddy. LLM-SR: Scientific equation discovery via programming with large language models. In The Thirteenth International Conference on Learning Representations , 2025.

K. Singhal, T. Tu, J. Gottweis, R. Sayres, E. Wulczyn, M. Amin, L. Hou, K. Clark, S. R. Pfohl, H. Cole-Lewis, et al. Toward expert-level medical question answering with large language models. Nature Medicine , pages 1-8, 2025.

H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 , 2023.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin. Attention is all you need. Advances in neural information processing systems , 30, 2017.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems , NIPS'17, page 6000-6010, Red Hook, NY, USA, 2017. Curran Associates Inc.

R. Wang, Y. Yuan, S. Sun, and J. Bian. Text-to-cad generation through infusing visual feedback in large language models. arXiv preprint arXiv:2501.19054 , 2025.

S. Wang, C. Chen, X. Le, Q. Xu, L. Xu, Y. Zhang, and J. Yang. Cad-gpt: Synthesising cad construction sequence with spatial reasoning-enhanced multimodal llms. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 39, pages 7880-7888, 2025.

R. Wu, C. Xiao, and C. Zheng. Deepcad: A deep generative network for computer-aided design models. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 6772-6782, 2021.

S. Wu, A. H. Khasahmadi, M. Katz, P. K. Jayaraman, Y. Pu, K. Willis, and B. Liu. Cadvlm: Bridging language and vision in the generation of parametric cad sketches. In European Conference on Computer Vision , pages 368-384. Springer, 2024.

J. Xu, Z. Zhao, C. Wang, W. Liu, Y. Ma, and S. Gao. Cad-mllm: Unifying multimodalityconditioned cad generation with mllm. arXiv preprint arXiv:2411.04954 , 2024.

X. Xu, P. K. Jayaraman, J. G. Lambourne, K. D. Willis, and Y. Furukawa. Hierarchical neural coding for controllable CAD model generation. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, and J. Scarlett, editors, Proceedings of the 40th International Conference on Machine Learning , volume 202 of Proceedings of Machine Learning Research , pages 38443-38461. PMLR, 23-29 Jul 2023.

X. Xu, J. Lambourne, P. Jayaraman, Z. Wang, K. Willis, and Y. Furukawa. Brepgen: A b-rep generative diffusion model with structured latent geometry. ACM Transactions on Graphics (TOG) , 43(4):1-14, 2024.

X. Xu, K. D. Willis, J. G. Lambourne, C.-Y. Cheng, P. K. Jayaraman, and Y. Furukawa. SkexGen: Autoregressive generation of CAD construction sequences with disentangled codebooks. In K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, and S. Sabato, editors, Proceedings of the 39th International Conference on Machine Learning , volume 162 of Proceedings of Machine Learning Research , pages 24698-24724, 17-23 Jul 2022.

A. Yang, B. Yang, B. Zhang, B. Hui, B. Zheng, B. Yu, C. Li, D. Liu, F. Huang, H. Wei, et al. Qwen2. 5 technical report. arXiv preprint arXiv:2412.15115 , 2024.

S. Yang, J. Liu, R. Zhang, M. Pan, Z. Guo, X. Li, Z. Chen, P. Gao, H. Li, Y . Guo, et al. Lidar-llm: Exploring the potential of large language models for 3d lidar understanding. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 39, pages 9247-9255, 2025.

F. Yu, Q. Chen, M. Tanveer, A. Mahdavi Amiri, and H. Zhang. D2csg: Unsupervised learning of compact csg trees with dual complements and dropouts. Advances in Neural Information Processing Systems , 36, 2024.

X. Yuan, C. Shen, S. Yan, X. Zhang, L. Xie, W. Wang, R. Guan, Y. Wang, and J. Ye. Instanceadaptive zero-shot chain-of-thought prompting. arXiv preprint arXiv:2409.20441 , 2024.

Y. Yuan, S. Sun, Q. Liu, and J. Bian. Cad-editor: A locate-then-infill framework with automated training data synthesis for text-based cad editing. arXiv preprint arXiv:2502.03997 , 2025.

Y. Zang, W. Li, J. Han, K. Zhou, and C. C. Loy. Contextual object detection with multimodal large language models. International Journal of Computer Vision , 133(2):825-843, 2025.

A. Zhang, W. Jia, Q. Zou, Y. Feng, X. Wei, and Y. Zhang. Diffusion-cad: Controllable diffusion model for generating computer-aided design models. IEEE Transactions on Visualization and Computer Graphics , 2025.

R. Zhang, J. Han, C. Liu, A. Zhou, P. Lu, Y. Qiao, H. Li, and P. Gao. LLaMA-adapter: Efficient fine-tuning of large language models with zero-initialized attention. In The Twelfth International Conference on Learning Representations , 2024.

Y. Zhang, F. Feng, J. Zhang, K. Bao, Q. Wang, and X. He. Collm: Integrating collaborative embeddings into large language models for recommendation. IEEE Transactions on Knowledge and Data Engineering , 2025.

Z. Zhang, M. Chen, Z. Gu, X. Zhao, Z. Yang, B. Lin, D. Cai, and W. Wang. Straj: Self-training for bridging the cross-geography gap in trajectory prediction. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 39, pages 22723-22731, 2025.

Z. Zhang, S. Sun, W. Wang, D. Cai, and J. Bian. FlexCAD: Unified and versatile controllable CAD generation with fine-tuned large language models. In The Thirteenth International Conference on Learning Representations , 2025.

Y. Zhou, L. Liu, and C. Gou. Learning from observer gaze: Zero-shot attention prediction oriented by human-object interaction recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 28390-28400, 2024.

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Justification: We describe the usage of LLMs in Sec. 4.

Guidelines:

The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.

Please refer to our LLM policy ( https://neurips.cc/Conferences/2025/LLM ) for what should or should not be described.

Appendix

Due to space limitations in the main paper, we provide additional results and discussions in this appendix, organized as follows:

Sec. A: More Details about VLLM-based Captioning.

Sec. B: Detailed Comparison with Existing Work.

Sec. C: Detailed Categories of Simple Parts and Complex Parts.

Sec. D: Details about Metrics for Evaluating Text-to-CAD consistency.

Sec. E: LLMs of Different Scales.

Sec. F: Sensitivity Analysis of Key Hyper-parameters in Sampling.

Sec. G: Criteria for Dataset Selection.

Sec. H: Sketch-level Editing.

Sec. I: Failure Cases, Limitations and Future Work.

Sec. J: Five-shot Prompt Example.

Sec. K: Additional Qualitative Results.

A More Details about VLLM-based Captioning

The prompt used for VLLM-based captioning is as follows:

"Given a loop in a CAD sketch, provide a brief description of its geometric shape starting with 'a' or 'an' if identifiable; otherwise, state 'None'."

Using this prompt, we randomly caption 1k complex local parts with GPT-4o [1] and Qwen2.5-VL72B-Instruct [4], respectively. Regardless of whether these models output a specific shape or 'None', we manually evaluate each result by judging its correctness as either "Yes" or "No". The overall captioning accuracy across these 1,000 parts is 91.3% for Qwen2.5-VL-72B-Instruct and 86.5% for GPT-4o. These results indicate that Qwen2.5-VL-72B-Instruct outperforms GPT-4o in this captioning task, which is consistent with with the latest multimodal model leaderboard rankings. Furthermore, given the lower cost of Qwen2.5-VL-72B-Instruct, we use it to caption the remaining complex parts.

B Detailed Comparison with Existing Work

As mentioned in lines 36-48 in our main paper, existing work struggles to achieve local geometrycontrollable CAD generation. Here, we further highlight the differences between CAD-Editor [55], FlexCAD [61] and our GeoCAD. CAD-Editor has difficulty focusing on local generation for two main reasons: 1) It may unintentionally modify the remaining parts, resulting in outputs that do not align with user requirements (as illustrated in the last example of Fig. 1 in the original CAD-Editor paper). 2) CAD-Editor fails to accurately obtain angle and length information, making it incapable of generating even simple parts, such as a right triangle, let alone an isosceles right triangle, as mentioned in line 45 of our main paper. FlexCAD, on the other hand, can focus on local parts but incorporates minimal geometric constraints, thereby struggling to follow geometric instructions. In particular, FlexCAD is unable to understand, let alone follow, simple or complex geometric instructions. This limitation is clearly demonstrated in Fig. 1 of our main paper.

C Detailed Categories of Simple Parts and Complex Parts

The categories of simple parts include acute triangle, right triangle, obtuse triangle, isosceles triangle, isosceles right triangle (Notably, equilateral triangles do not occur in the DeepCAD [45] dataset), quadrilateral, trapezoid, isosceles trapezoid, kite (Two pairs of adjacent sides equal), parallelogram, rectangle, rhombus, square, circle, semicircle, quarter-circle, three-quarter circle, major-arc loop (defined as containing an arc longer than a semicircle), minor-arc loop (defined as containing an arc shorter than a semicircle), and so on. The remaining local parts are classified as complex, exhibiting more intricate and diverse visual patterns.

D Details about Metrics for Evaluating Text-to-CAD consistency

As mentioned in lines 213-217 of our main paper, we employ Ver-score , VLLM-score , and Realism to comprehensively evaluate model performance in terms of text-to-CAD consistency. Specifically, to compute Ver-score , we extract vertex coordinates from the generated local parts and analyze their geometric attributes to determine whether they align with the given geometric instructions. To obtain VLLM-score , we first render the local parts into images and then prompt two of the most powerful VLLMs, GPT-4o [1] and Qwen2.5-VL-72B-Instruct [4], to judge whether the rendered images match the corresponding instructions, assigning a binary label: "Yes" or "No." We report the average of their scores in Table 1 of our main paper, where both models significantly outperform the baselines. To evaluate Realism , we randomly render 500 newly generated CAD models into images, with the modified local parts clearly marked. Five crowd workers are then asked to assess whether the generated local parts align with the geometric instructions and do not conflict with the remaining parts. If both criteria are satisfied, they assign a binary label: "Yes"; otherwise, "No." The average score from these workers is reported in Table 1 of our main paper.

E LLMs of Different Scales

As shown in Table 3, Transformer -4M achieves the lowest performance, confirming that LLMs play a key role in enhancing local CAD generation. Llama -3 -8B -Full performs comparably to Llama -3 -8B , demonstrating the effectiveness of the LoRA strategy [16]. As two of the most popular open-source LLMs, Qwen2 . 5 -7B -Instruct slightly outperforms Llama -3 -8B .

F Sensitivity Analysis of Key Hyper-parameters in Sampling

As shown in Table 4, we conduct a sensitivity analysis on key hyperparameters, including the sampling temperature τ and Top -p . All other experimental settings follow those described in Section 4.2 of our main paper. In general, increasing τ or Top -p results in more diverse and stochastic predictions. However, this comes at the cost of reduced PV , while other metrics tend to improve, consistent with

findings in [61]. In our experiments, we balance this trade-off by selecting τ and Top -p values that ensure the PV remains above 90%.

G Criteria for Dataset Selection

DeepCAD [45] is a suitable dataset for evaluation, and the reasons are detailed below: 1) Scale: DeepCADis a large-scale 3D CAD dataset, comprising over 178k samples. 2) Relevance to Controllability: Compared to 2D sketch datasets, DeepCAD better reflects the requirements of controllable generation, as aligning local parts within 3D CAD models is both more challenging and more practical. 3) Design Process Alignment: In contrast to other 3D CAD datasets, such as the ABC dataset [19], DeepCAD includes sketch-and-extrusion sequences that closely mirror the design workflows of commercial CAD tools like SolidWorks and AutoCAD. 4) Community Adoption: Due to its characteristics, DeepCAD is also the only choice for prior studies, including SkexGen [50], HNC-CAD [48], CAD-Editor [55], CADFusion [44], Text2CAD [18], and FlexCAD [61].

reflection

a rectangle

H Sketch-level Editing

For sketch-level editing, if a sketch contains multiple loops, ideally, we would like to learn the inter-loop constraints ( e.g. , symmetry, patterns, etc.) that define the overall structure. However, as mentioned above, DeepCAD is currently the only dataset suitable for controllable 3D CAD generation, and unfortunately, such inter-loop constraint annotations are not provided in the dataset. Fortunately, even without supervision from these constraints, sketch-level editing is still achievable based on our loop-level editing capability. This is because the loop serves as the fundamental element of a sketch. For example, as shown in Fig. A1, if a user selects a sketch consisting of two symmetric loops and wishes to replace them with another pair of symmetric loops, the following automatic process can be performed: 1) Estimate the center point of each original loop by averaging its coordinate points, which are extracted using string matching. 2) Determine the symmetry axis based on the two center points. 3) Generate a new local loop through GeoCAD replacing one of the orignal loops. 4) Reflect the newly generated loop across the symmetry axis to produce the second symmetric loop, thereby replacing both original loops.

I Failure Cases, Limitations and Future Work

an irregular shape with two

large, rounded lobes connected

by a narrow isthmus

an irregular polygon

resembling a six-pointed

star with extended arms

a semicircle connected to a

rectangle below it

a quarter-circle

an isosceles trapezoid

a semicircle connected to a

rectangle below it

Failure cases. Despite notable advancements, our GeoCAD sometimes results in failure cases. As shown in Fig. A2, given a CAD model, when only the special part is modified ( i.e. , the part upon which the remaining parts are constructed and strictly aligned in size), the unchanged remaining parts may lead to structural conflicts with it. To mitigate this issue, when modifying the special parts, users should provide geometric instructions that account for the constraints imposed by the remaining parts, since the DeepCAD dataset does not annotate the relationships between different parts.

Limitations and future work. In this paper, we fine-tune LLMs to enable local geometry-controllable CADgeneration, primarily guided by textual instructions. However, in practice, certain complex local

parts may be difficult or even impossible to describe using text alone. Thus, in the future, if users can complement textual inputs with hand-drawn images for local geometry-controllable CAD generation, they may be able to convey their design intent more effectively. Given the strong capabilities of VLLMs in both CAD generation and text understanding, our future work aims to develop a more advanced multimodal LLM tailored for controllable CAD generation from both text and image inputs.

J Five-shot Prompt Example

To better illustrate the implementation details of the baselines and our GeoCAD in Table 1 of our main paper, we present a five-shot prompt example, as shown in Fig. A3.

K Additional Qualitative Results

We provide additional qualitative results in Fig. A4.

a quarter circle

an ellipse with

rectangular extensions

at both the top and

bottom

a rectangle missing

one corner

a polygon that looks

like an irregular cross

shape

a broken ring

shape

a shape that looks

like a water droplet

a major-arc

connected to a

rectangle

a heart-shaped loop

a shape that

looks like a

semicircular ring

a shape formed by

two smoothly

connected circles

a cross-shaped loop

a semicircle

connected to a

rectangle below it

a quarter circle

connected to a

rectangle

a rounded rectangle

with a circle at the

top and bottom

an irregular polygon

with a pointed tip at

the top

a rectangle with a

triangular notch

at the bottom

a rectangle with a

at the bottom

an irregular polygon

a rectangle

connected to

a semicircle

a rectangle with

four pointed corners

and four notches

a smooth cross-

shaped loop

a letter 'T'

a letter 'I'

a letter 'U'

a letter 'L'

a letter 'H'

a letter 'C'

a letter 'Y'

a letter 'I'

a letter 'V'

a letter 'X'

a letter 'T'

a letter 'L'

a letter 'U'

a letter 'L'

a letter 'V'

a letter 'H'

a letter 'X'

a letter 'T'

a regular polygon

with 8 sides

a square

a regular polygon

with 6 sides

an elongated rectangle

a rectangle

a rhombus

a right trapezoid

an isosceles trapezoid

an irregular

quadrilateral

a rhombus

a square

a circle

a rectangle with

rounded corners

and four notches

a rectangle with a

at the top

a rectangle with

a U-shaped notch

at the top

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes GeoCAD, a local geometry controllable generation method. It can take the original CAD model and user-specific geometric instructions  as inputs. It is one of the first model to achieve geometry control in the generation of CAD.  Extensive results shows that the proposed GeoCAD approach significantly enhances generation quality, validity, and text-to-CAD consistency in local geometry-controllable CAD generation.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n- The motivation of this paper sounds reasonable. GeoCAD can achieve local geometry-control in the CAD generation field. \n- The two stage training pipeline is novel and effective. Different data augmentation strategies are used in stage 1.\n- The paper is well written and easy to follow. Main figures in this paper are well drawn and make the method clear to understand.\n- The experiments are done comprehensively and there are a lot of qualitative results given, demonstrating the superiority of the proposed GeoCAD method compared with other baselines such as OpenAI-o3 and FlexCAD.\n\nWeaknesses:\n- The authors claim that VLLMs exhibit limitations in fine-grained geometric descriptions for simple parts. For instance, they struggle to reliably discern whether a quadrilateral is a rhombus according to an image alone. But this is well supported. Actually state-of-the-art VLLMs can already perform well on existing benchmarks asking for geometric understanding.\n\n- The quality of the annotated 221k local parts are not clear. Human evaluation for at least part of them is needed.\n\n- The LLM is fine-tuned using LoRA, while it lacks comparisons on using other parameter efficient finetuning strategies as well as full-model finetuning. It is also not sure if the rank of 8 and an alpha of 3 is the optimum setting for the finetuning.\n\nQUESTIONS:\nI wonder if the scaling law also happens here when evaluating the LLMs’ ability in predicting the masked local part. Will larger LLMs perform better on this CAD task after the proposed finetuning strategy?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 1, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-23"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a method to support user-friendly CAD model editing through prompts. Using prompts to edit CAD models is an interesting problem; this method additionally requires the user to select a local profile to be edited. The technical contributions focus on: 1. The captioning strategy - it creates a prompt-to-local-part dataset suitable for both simple and complex parts generation. 2. The pre-training strategy on the prompt-to-local-part dataset, which enables the VLLM to learn local parts generation better. The experiments show that the method can produce better results than several baselines and can follow prompts to generate desired local profiles.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1.The proposed problem is novel; using natural language to efficiently edit CAD models is an interesting problem.\n\n2.The performance is impressive compared to several baselines.\n\n3.The paper is well-structured and easy to understand. The experimental results are also promising.\n\nWeaknesses:\n\n1.The input requires a clean CAD shape program, which limits the application scope. Many CAD shapes in the ABC dataset cannot be edited by this method.\n\n2.The editing operations have many limitations. Many local patterns are difficult to describe through text. Additionally, users can only edit the 2D sketch, not the extrusion length. The text prompt will change the entire 2D sketch selected by the user, not just part of the sketch. I don't see more efficiency or flexibility compared to manual editing of 2D sketches. \n\n3.The authors proposed two main technical contributions - the separated captioning strategy and the pre-training - which appear very intuitive and specifically designed only for this task.\n\nOverall, I feel the technical contributions are not significant, and the use case is not quite general. There are many limitations that need to be addressed for more practical use cases.\n\nQUESTIONS:\nIs it possible to also use prompt language to change the extrusion length and the extrusion combination operations (union and differ)? It would be better to discuss this point.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 1, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThey tackle the problem of \"geometry-controllable CAD\" generation, swhich aims to modify specific parts of CAD models, instead of generating everything from scratch.\n\n  They introduce a vertex-basesd and VLLM-based captioning system for annotating simple and complex parts.\n  They use this appraoch to create a dataset of annotations of local parts, used ot train an LLM.\n\n  The method takes in 3 inputs: FlexCAD-style CAD model to be edited, the Local part designated for modification, and geometric instructions by the user. It ouptuts a new CAD model.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n  + The problem of being able to *edit* CAD in a locally controllable way is perhaps more impactful than the ability to *generate* from scratch, given the amount of time engineers spend editing their designs.\n\n  + Substantial work has gone into generating the captioning of local parts, and a sizeable dataset is produced. Both sound like solid contributions to the field.\n\n  + Evaluation metrics seems to be comprehensive. \n\nWeaknesses:\n\n  + The captioning method and geometric processing seem to rely on the need for the operations to compose mostly of curves + extrusions, which limits the space of shapes that this can be applied onto. Have you thought about how such methods can be used for more general, complex shapes?\n\n  + The language captions broadly describe the category of geometric shape, but it's unclear how this method would fare with more specific language instructions, like \"a rectangle cutout that's 45 degrees to the nearest outer wall of the shape\"\n\nQUESTIONS:\nFigure 8 shows the ability for the model to understand measurements. Is this from the LLM base understanding from Llama pretraining, or were there captions in your dataset that contained dimensional geometric instructions?\n\n  On average, how many CAD instructions compose the shapes? What is the maximum number of CAD instructions that the base LLM (llama-3-8b) able to handle (both theoretically and in practice)?\n\n  Is the model capable of doing mutliple rounds of consecutive edits, iteratively? Especially when the second instruction depends on the successful execution of the first instruction?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a method for performing local edits in parametric CAD models represented as a sequence of sketch-extrude commands. The method works by converting the CAD model commands into a sequence of text-based tokens resembling the xml format. This format is adopted from FlexCAD and importantly captures hierarchical geometric information in the sketch such as curves, loops and faces. Based on this format, a masking strategy is introduced on the loop-level and an LLM is fine tuned to predict the masked commands representing the loop, while also being conditioned by an input text prompt. The text prompt is a description of the loop to be infilled by the LLM. For training, these prompts are obtained by a combination of vLLM labeling + heuristics. This strategy allows local edits to be performed on the sketches while ensuring that the rest of the CAD model is updated in a consistent manner. Results demonstrated on the DeepCAD dataset show that the proposed method outperforms FlexCAD and OpenAI’s o3 model in instruction following and validity of the synthesized edits.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n- the paper is well written and the critical design choices of masking loops, finetuning the LLM with LoRA, generating captions using vision language models, are all very sensible. Ablation studies evaluate the critical design choices and demonstrate their significance.\n- the demonstrated results are strong and show excellent instruction following by the proposed model, while preserving the design intent in the rest of the CAD model.\n\nWeaknesses:\n- Novelty: the proposed approach closely follows FlexCAD with some small but notable improvement in the training strategy. Consequently, technical novelty is limited, but results are better particularly in terms of the instruction following. \n- Practicality: the captions and loop-level edits used in the paper are quite simple. Loop level edits are quite low-level for CAD designers to work with, and can be quickly done manually. Sketch level editing would have been a good use case to demonstrate, but it appears that this wasn’t explored. It is also important to capture and measure if design intent is preserved, for example if there are two symmetric circles in a face, and one circle edited, does the other one get updated automatically? Having to manually constrain the sketch and/or apply dimensions to each loop is quite tedious. Similarly, more complex shapes loop level edits aren’t shown in the paper. The captions are fairly low level geometric descriptions. To give an example, changing the number of teeth in a gear, or reducing the dimension of an outer loop which automatically updates the inner loop geometries while preserving design intent (symmetries, patterns etc.) would be more practical for designers. It feels like these are within grasp with this method, but weren’t explored in the paper.\n\nQUESTIONS:\n- Can the authors demonstrate examples where multiple loops or faces are masked and edits that are more complex & higher level are performed? This is more in line with what designers expect from a text based editing system. Working with individual loops feels too low-level (it’s fine to train the model this way though) and demonstrating this capability can significantly improve the practical implications of this work.\n- Can the authors demonstrate if design intent is preserved when editing the shape? A simple example would be (but please don’t be limited to this) to gradually reduce the dimensions of the outer loop and evaluate if the inner loops are updated sensibly without self intersections or symmetries breaking. A crucial requirement for editing workflows is to preserve design intent, and if the method can be demonstrated to do this, then the potential of this approach to work with higher level semantic edits in future will be very clear.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-06"}